CN116807403A - Simultaneous disambiguation detection method based on scale - Google Patents
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Abstract
The invention provides a simultaneous failure syndrome detection method based on a scale. The method comprises the following steps: designing and determining a scale comprising a plurality of pictures, each picture being a larger geometric figure composed of a plurality of identical smaller basic geometric figures; drawing up test questions based on the scale, and adopting the test questions to check patients, wherein the patients are required to respectively identify smaller basic geometric figures and larger geometric figures in the pictures; and obtaining the assessment score of the patient, and obtaining the quantitative evaluation of the severity of the simultaneous disbelief of the patient. The invention can effectively detect the severity of the simultaneous disbelief of the patient by detecting the patient based on the scale, and can also detect the ability of the patient to recognize different shapes.
Description
Technical Field
The invention belongs to the technical field of medicine, and particularly relates to a simultaneous failure syndrome detection method based on a scale.
Background
Simultaneous misregistration is a visual misregistration in which a patient can recognize individual objects or characters in a picture or scene, but cannot properly understand the entire picture or scene. While the manifestations of the disorder are characterized, they are easily confused with other ocular diseases or diseases of the visual space. The detection of simultaneous disbelief at the present stage mainly depends on the description of the patient and the detection of imaging. The tools for detecting the simultaneous failure are not uniform, and measuring tools which are widely applied are not available. At present, various measuring tools require patients to recognize English letters or understand English life scenes, and a higher education level is required. And all simultaneous misregistration at present only can reflect that a patient cannot recognize a scene, but cannot reflect the other important characteristic of the simultaneous misregistration, namely the retention of the individual picture recognition capability. This makes current measurement tools unable to identify other ocular diseases.
Thief cookie graphic testing is primarily used to test whether a patient with alzheimer's disease can logically describe a picture, and is later used to identify simultaneous disbeliefs, so it is difficult to exclude the effects of language ability. The test of Navon requires that the subject recognize a large letter consisting of a small capital letter. The overlapped graph test is a complex graph overlapped by different graphs, and is mainly and widely applied to the test of simultaneous disbelief. Different overlay tests were made using different numbers and shapes. The problem with these simultaneous failure tests is that they reflect only a reduction in the overall visual processing capacity of the patient, and do not reflect another important feature of the simultaneous failure symptoms, namely the retention of individual picture recognition capacity.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a simultaneous failure syndrome detection method based on a scale.
In order to achieve the above object, the present invention adopts the following technical scheme.
A simultaneous failure detection method based on a scale comprises the following steps:
designing and determining a scale comprising a plurality of pictures, each picture being a larger geometric figure composed of a plurality of identical smaller basic geometric figures;
drawing up test questions based on the scale, and adopting the test questions to check patients, wherein the patients are required to respectively identify smaller basic geometric figures and larger geometric figures in the pictures;
and obtaining the assessment score of the patient, and obtaining the quantitative evaluation of the severity of the simultaneous disbelief of the patient.
Further, the method for determining the scale comprises the following steps:
designing a plurality of scales to be selected;
transmitting the plurality of candidate scales to an expert group of neurological and psychological experts for voting;
and determining a scale according to the voting result.
Still further, the smaller base geometric figures include regular triangles, squares and circles; the larger geometric figures also include regular triangles, squares and circles, and can be larger solid geometric figures which are fully populated with the smaller basic geometric figures, or larger hollow geometric figures which are only marginally populated with the smaller basic geometric figures.
Still further, the test questions include a local test question and an overall test question: the local test questions require that smaller basic geometric figures are identified and used for detecting the identification capability of a patient on the shape of a single object; the overall test question requires that a larger geometry, consisting of a smaller basic geometry, be identified for detecting the patient's ability to identify the overall shape.
Further, the test questions comprise 18 different scale pictures, wherein the number of each picture is 3, and the total number of the pictures is 54; each picture is used as 1 local test question and 1 whole test question, and 54 local test questions and 54 whole test questions are obtained in total; each test question answering pair is given a score of 1, and the wrong answer does not score.
Further, if a smaller basic geometric figure or a larger geometric figure cannot be identified within a set time, the corresponding test question does not score.
Further, the patient's assessment score is equal to the difference between the local test question score and the overall test question score; the higher the assessment score, the more serious the malaise.
Further, the method further comprises: and evaluating the scale by carrying out statistical calculation on the assessment score of the parameter evaluation personnel, and correcting the scale based on the evaluation result.
Still further, the method of evaluating the scale includes:
determining a panelist participating in a scale assessment, the panelist comprising a posterior cortical atrophy patient and an alzheimer patient, and a cognitive normative person age-matched to the patient, having no family history of dementia or mental illness;
adopting the test questions to check the reference and evaluation personnel, and carrying out reliability analysis, effectiveness analysis, optimal cut-off point and ROC curve analysis on the scale based on the check scores, wherein the method specifically comprises the following steps of:
verifying the internal consistency of the scale by using Cronbach's alpha coefficient;
re-testing reliability and inter-testing personnel reliability by adopting a correlation coefficient evaluation scale;
content effectiveness of a correlation coefficient evaluation scale is adopted;
determining the feasibility of factor analysis by adopting a KMO test and a Bartlett's test; adopting principal component analysis and a maximum variance rotation test scale to obtain structural effectiveness;
the Pearson correlation coefficient is adopted to evaluate the correlation between the effective mark degree and the overall test and the scale score;
drawing a working characteristic curve of a parametrier, namely an ROC curve, analyzing the sensitivity and the specificity of the scale, and determining the intercept point of the scale.
Still further, the method for revising the scale based on the evaluation result includes:
and according to the effectiveness analysis, if the Pearson correlation coefficient of a certain local test question score and all local test questions score of the commender is smaller than a set threshold value or the Pearson correlation coefficient of a certain overall test question score and all overall test questions score of the commender is smaller than a set threshold value, deleting the local test questions or the overall test questions, and deleting the other 2 local test questions or the overall test questions using the same picture with the local test questions or the overall test questions.
Compared with the prior art, the invention has the following beneficial effects.
According to the invention, a scale comprising a plurality of pictures is designed and determined, each picture is a larger geometric figure formed by a plurality of same small basic geometric figures, test questions are formulated based on the scale, the test questions are adopted to examine a patient, the patient is required to respectively identify the small basic geometric figures and the larger geometric figures in the pictures, the examination score of the patient is obtained, and the quantitative evaluation of the severity of simultaneous disbelief of the patient is obtained. The invention can effectively detect the severity of simultaneous disbelief of the patient by detecting the patient based on the scale, and can also detect the ability of the patient to recognize different shapes.
Drawings
Fig. 1 is a flowchart of a simultaneous failure detection method based on a scale according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of 18 pictures of a scale according to an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the drawings and the detailed description below, in order to make the objects, technical solutions and advantages of the present invention more apparent. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flowchart of a simultaneous failure detection method based on a scale according to an embodiment of the present invention, including the following steps:
step 101, designing and determining a scale comprising a plurality of pictures, wherein each picture is a larger geometric figure formed by a plurality of same smaller basic geometric figures;
102, setting up a test question based on the scale, and checking a patient by adopting the test question, wherein the patient is required to respectively identify a smaller basic geometric figure and a larger geometric figure in a picture;
and step 103, obtaining the assessment score of the patient, and obtaining the quantitative evaluation of the severity of the simultaneous disbelief of the patient.
In this embodiment, step 101 is mainly used to design and determine a scale for simultaneous failure detection. The scale consists of a plurality of pictures, as shown in fig. 2, each picture comprising a number of smaller basic geometries, all of which are identical, e.g. all triangular or all square, etc.; these smaller base geometries are in turn laid out as a larger geometry which may be identical to the smaller base geometries, such as a larger triangle from a number of smaller triangles; or may be different, such as by a number of smaller triangles that are arranged in a larger square.
In this embodiment, step 102 is mainly used for formulating examination questions based on the scale. Typically, a chart corresponds to a test question (the test question can be reused, for example, 3 test questions use the same picture), and the tested patient is required to identify a smaller basic geometric figure and a larger geometric figure in the picture. The ability to identify larger geometric figures can reflect the severity of simultaneous failure, while the ability to identify smaller basic geometric figures can be used to test the ability to identify different shapes.
In this embodiment, step 103 is mainly used for quantitatively evaluating the severity of simultaneous disbelief of patients. The present example uses the assessment score of the patient being tested to give a quantitative assessment of the severity of simultaneous disbelief. In order to enable the assessment score to intuitively reflect the severity of the simultaneous failure to be identified, the severity of the simultaneous failure to be identified can be positively correlated with the assessment score by designing a scoring mechanism, namely, the higher the assessment score is, the more serious the simultaneous failure to be identified is.
According to the embodiment, the scale for simultaneous failure detection is designed, and the patient is detected based on the scale, so that the severity of the simultaneous failure of the patient can be effectively detected, and the ability of the patient to identify different shapes can be detected.
As an alternative embodiment, the method for designing and determining the gauge includes:
designing a plurality of scales to be selected;
transmitting the plurality of candidate scales to an expert group of neurological and psychological experts for voting;
and determining a scale according to the voting result.
The embodiment provides a technical scheme for designing and determining the scale. In the embodiment, an expert group is built, evaluation voting is carried out on a plurality of designed to-be-selected scales by the expert group, and scales meeting requirements are screened out according to voting results. The expert group consisted of neurological and psychological experts, such as an expert group consisting of 22 members, 8 of which had clinical and research expertise in neurology and psychology, and 14 of which had expertise in neurology, psychology, scale development and clinical trials.
As an alternative embodiment, the smaller basic geometric figures include regular triangles, squares and circles; the larger geometric figures also include regular triangles, squares and circles, and can be larger solid geometric figures which are fully populated with the smaller basic geometric figures, or larger hollow geometric figures which are only marginally populated with the smaller basic geometric figures.
The present embodiment further defines the scale picture. In this embodiment, the smaller basic geometric figure and the larger geometric figure are selected from the most common and simplest 3 geometric figures, which are respectively triangle, square and circle, as shown in fig. 2. It should be noted that the larger geometry may be solid or hollow. Solid means that the whole larger geometric figure is fully distributed by the smaller basic geometric figure, and no gap is reserved in the middle; hollow refers to the fact that the middle of the larger geometry is hollow and the smaller basic geometry is placed only at the edges. Thus, 3×3×2=18 different scale pictures can be obtained from 3 geometries.
As an alternative embodiment, the test questions include a local test question and an overall test question: the local test questions require that smaller basic geometric figures are identified and used for detecting the identification capability of a patient on the shape of a single object; the overall test question requires that a larger geometry, consisting of a smaller basic geometry, be identified for detecting the patient's ability to identify the overall shape.
The present embodiment further defines the test questions. In this embodiment, the questions are classified into two categories: one type is a local test question, and the other type is an overall test question. The local test questions and the whole test questions adopt the same scale picture, the local test questions require to identify smaller basic geometric figures in the picture, and the whole test questions require to identify larger geometric figures formed by the smaller basic geometric figures. Local test questions may be used to detect the patient's ability to identify the shape of a single object, and global test questions may be used to detect the patient's ability to identify the global shape that is composed of individual objects. The recognition capability of the overall shape can better reflect the severity of the simultaneous failure to recognize, and generally, the weaker the recognition capability of the overall shape, the more serious the simultaneous failure to recognize.
As an optional embodiment, the test questions include 18 different scale pictures, and the number of each picture is 3, and total 54 pictures; each picture is used as 1 local test question and 1 whole test question, and 54 local test questions and 54 whole test questions are obtained in total; each test question answering pair is given a score of 1, and the wrong answer does not score.
The embodiment provides a specific test question setting. The test questions comprise 54 local test questions and 54 whole test questions, the 54 local test questions and the 54 whole test questions correspond to 54 pictures respectively, and the local test questions and the whole test questions adopt the same pictures, but the objects required to be identified are different. The 54 pictures comprise 18 different pictures, i.e. each picture is reused 3 times. The 18 pictures are larger triangles, squares and circles that are laid out by the smaller basic geometry triangles, squares and circles, resulting in a total of 3×3=9 larger geometries. The 9 larger geometries are further designed as solid and hollow, thus giving a total of 9×2=18 different pictures. For simple scoring, the score of each test question is set to be 1, the answer is 1, and the answer is wrong.
As an alternative embodiment, if a smaller basic geometry or a larger geometry is not identified within a set time, the corresponding test question does not score.
The embodiment provides a time-limited answering mechanism. In order to make the examination more reasonable, the embodiment limits the longest answer time of each test question, such as 5 seconds, if the answer is completed within 5 seconds, the answer is effective; if the result cannot be given for more than 5 seconds, the corresponding test question does not score.
As an alternative embodiment, the patient's assessment score is equal to the difference between the local test question score and the overall test question score; the higher the assessment score, the more serious the malaise.
The present embodiment provides a scoring mechanism. In order to quantitatively evaluate the severity of simultaneous disbelief of a tested patient by using the accuracy of the assessment score, the embodiment designs the final assessment score as the difference between the local test question score and the overall test question score. The design and assessment score can eliminate the influence of vision misregistration of the tested patient other than the simultaneity misregistration. As described above, the simultaneous disbelief patient can identify a single object, and the ability to identify the overall pattern is poor, i.e., the overall test question score is low; unlike simultaneity disapproval, other visual disapproval have little distinction in the impact of individual object recognition capability and overall pattern recognition capability, i.e., the local test question and overall test question scores are nearly equal. Therefore, by calculating the difference between the local test question score and the overall test question score, the effect of other visual misidentification can be eliminated.
As an alternative embodiment, the method further comprises: and evaluating the scale by carrying out statistical calculation on the assessment score of the parameter evaluation personnel, and correcting the scale based on the evaluation result.
The embodiment provides a technical scheme for correcting the scale. The scales determined in the previous examples, although subject to expert panel selection, are not necessarily the most effective scales. For this purpose, the present embodiment corrects the scale by evaluating the scale and correcting the scale based on the scale evaluation. In this embodiment, the evaluation scale is evaluated by performing statistical calculation and analysis on the assessment score of the reference person. The following examples will give a specific evaluation method.
As an alternative embodiment, the method for evaluating the scale includes:
determining a panelist participating in a scale assessment, the panelist comprising a posterior cortical atrophy patient and an alzheimer patient, and a cognitive normative person age-matched to the patient, having no family history of dementia or mental illness;
adopting the test questions to check the reference and evaluation personnel, and carrying out reliability analysis, effectiveness analysis, optimal cut-off point and ROC curve analysis on the scale based on the check scores, wherein the method specifically comprises the following steps of:
verifying the internal consistency of the scale by using Cronbach's alpha coefficient;
re-testing reliability and inter-testing personnel reliability by adopting a correlation coefficient evaluation scale;
content effectiveness of a correlation coefficient evaluation scale is adopted;
determining the feasibility of factor analysis by adopting a KMO test and a Bartlett's test; adopting principal component analysis and a maximum variance rotation test scale to obtain structural effectiveness;
the Pearson correlation coefficient is adopted to evaluate the correlation between the effective mark degree and the overall test and the scale score;
drawing a working characteristic curve of a parametrier, namely an ROC curve, analyzing the sensitivity and the specificity of the scale, and determining the intercept point of the scale.
The embodiment provides a technical scheme for scale evaluation. The person participating in the scale evaluation, i.e., the participant, is first determined. The criticizing personnel comprise two parts: a portion consisting of patients, including posterior cortical atrophy patients and alzheimer's patients; the other part consists of normal persons, including those with cognitive normals who are age-matched to the patient, have no family history of dementia or mental illness, and of course, have had the need to understand that testing is difficult or that ophthalmic illness is excluded. And then, checking the evaluation personnel by adopting the test questions, and obtaining check scores. The assessment score here includes not only the final assessment score of each person to be tested, but also the score of each test question, namely, the score detail. And finally, evaluating the scale by performing reliability analysis, effectiveness analysis, optimal cut-off point and ROC curve analysis based on the assessment score. The embodiment utilizes statistical analysis software SPSS to analyze reliability, effectiveness and the like, and specifically comprises the following steps S1 to S6:
s1, verifying the internal consistency of a scale by using Cronbach' S alpha coefficient.
The Cronbach's alpha coefficient is a statistic, which means the average value of the folded half confidence coefficients obtained by all possible project division methods of the table, and is the most commonly used confidence measurement method. In this embodiment, the SPSS software is used to obtain the α coefficient of the local test, the α coefficient of the global test, and the α coefficient of the scale by inputting the scores of the local test questions 1 to 54, the scores of the global test questions 1 to 54, and the scores of all 108 test questions, respectively. Taking the Alpha coefficient of the whole test as an example, clicking the analysis to Scale to Reliability Analysis on the SPSS main interface in sequence, putting the whole test 1-whole test 54 into an Items frame, setting the Model as Alpha, and outputting the Alpha coefficient of the whole test.
In one specific test evaluation, the obtained local test alpha coefficient, the whole test alpha coefficient and the scale coefficient are respectively 0.948, 0.960 and 0.953, which indicate that the scale has good internal consistency reliability.
S2, re-measurement reliability and inter-personnel reliability of the evaluation scale of the correlation coefficient are adopted.
As described above, 54 pictures corresponding to 54 test questions are obtained by repeating each of 18 different pictures 3 times, and the retest confidence level in this embodiment is the confidence level for each picture repeated 3 times. The method for obtaining the retest reliability comprises the following steps: and sequentially selecting analysis, scale and retension analysis from the SPSS, respectively placing Two groups of data (fractions) of the first test and retest into Items frames, clicking statics, selecting Intraclass correlation coefficient, setting Model as Two-way range, and outputting retested correlation coefficients of the Scale.
The method for obtaining the credibility among the evaluation personnel comprises the following steps: the assessment scores of the same tested personnel in charge of any Two assessment personnel (generally acted by doctors) are respectively put into Items frames, statistics is clicked, intraclass correlation coefficient is selected, model is set as Two-way range, and a correlation coefficient reflecting the credibility among the assessment personnel is output.
In the test evaluation, the obtained scale overall re-measurement reliability ICC was 0.906, which was greater than 0.7, and was excellent. The evaluation of 10 test persons was completed simultaneously by 2 evaluation persons (clinicians), and the inter-evaluation-person confidence ICC was 0.921, which was also quite high.
S3, adopting a content effectiveness degree of a correlation coefficient evaluation scale.
And calculating the content validity of the correlation coefficient evaluation scale of the whole test questions 1-54 and the whole test questions by calculating the correlation coefficients of the local test questions 1-54 and the local test questions. The correlation coefficient between the local test question 1 and the local test question is equal to the score (x) of the local test question 1 of n tested persons 11 ,x 12 ,…,x 1n ) Total score of all local test questions with n subjects (x p1 ,x p2 ,…,x pn ) Is used for the correlation coefficient of the (c). Other correlation coefficients are analogized in turn.
In the test evaluation, the correlation coefficient between the local test questions 1 to 18 and the local test questions is 0.5 to 0.9. The correlation coefficients of the total questions 1, 2, 9, 10, 15 and 16 and the total questions are all smaller than 0.4. The 6 questions with the correlation less than 0.4 are all the same questions (such as triangle is put into triangle) of large figure (short for larger geometric figure) and small figure (short for smaller basic geometric figure).
S4, determining the feasibility of factor analysis by using Kaiser-Meyer-Olkin (KMO) test and Bartlett' S test; the structural effectiveness of the principal component analysis and maximum variance rotation test scale is adopted.
The KMO test is an index for comparing simple correlation coefficients and partial correlation coefficients between variables. The method is mainly applied to factor analysis of multivariate statistics. The KMO statistic has a value between 0 and 1. The Bartlett's test is a test for correlation between test variables in factor analysis, where KMO values are close to 1 when correlation is strong. The realization method using SPSS comprises the following steps: in SPSS, all the questions of analysis, dimension reduction, factor, CFT-S are selected, KMO test and Bartlett' S test are selected, the output value is outputted, and feasibility is judged according to the value. In the test evaluation kmo= 0.831, greater than 0.8, was obtained, demonstrating that factor analysis is feasible.
The structural effectiveness of the scale is to illustrate how well the results obtained from the scale match the theory that the scale was designed to measure. The structural effectiveness analysis generally adopts factor analysis to observe whether each index has a large load on the factor.
The realization method using SPSS comprises the following steps: in the SPSS, all the problems of Analyze, dimension reduction, factor, CFT-S, KMO test and Bartlett' S test, extraction and selection of principal component analysis methods, other default, rotation of a tab, selection of a maximum variance method and selection Factor load of more than 0.6 are sequentially selected. In the test evaluation, the factor 1 comprises a whole test item with inconsistent small patterns and large patterns, the factor 2 comprises a local test item with inconsistent small patterns and large patterns, (such as a large square formed by small circles), the factor 4 comprises a whole test with consistent small patterns and large patterns, the factor 3 comprises a test question with consistent small patterns and large patterns (such as a large circle formed by small circles) in the local test, the factor 5 comprises a local test question 8, and the factor 6 comprises a whole test question 10.
S5, evaluating the effectiveness and the effectiveness degree by using a Pearson correlation coefficient, and testing the correlation between the overlay and scale scores;
calibration effectiveness refers to the relationship between the research tool and other measurement criteria. The present embodiment adopts the overlay test as the corresponding measurement index. The realization method using SPSS comprises the following steps:
sequentially selecting Analyze, correlay, bivariate, selecting variable overall test and overlapping graph test in the SPSS, selecting Pearson of Correlation Coefficients areas in the Variables box, selecting Bivariate Correlation dialog box, and outputting Pearson correlation coefficient of local test question 1. The correlation coefficients of the whole test questions 1 to 54 and the local test questions are calculated according to the step.
Sequentially selecting Analyze, correlay, bivariate, selecting CFT-S test and overlapping graph test in the SPSS, selecting Pearson in the Variables box, selecting Correlation Coefficients area in the Bivariate Correlation dialog box, and outputting the Pearson correlation coefficient of the whole test question 1. The correlation coefficients of the overall test 1-54 and the overall test questions were calculated in this step.
In the test evaluation, the overall test score and the overlay test score are positively correlated (r=0.667, p < 0.01), the difference score and the overlay test score are negatively correlated (r= -0.670, p < 0.01), and the calibration efficiency of the scale is good.
And S6, drawing a parametric evaluation personnel working characteristic (ROC) curve, analyzing the sensitivity and the specificity of the scale, and determining the intercept point of the scale.
The ROC curve is a curve with sensitivity on the Y axis and (1-specificity) on the X axis. The realization method using SPSS comprises the following steps:
and (3) sequentially selecting Analyze, ROC curve, selecting score difference, selecting PCA group of groups, and generating an ROC curve on an SPSS interface.
The cut-off point is determined by calculating the maximum value of sensitivity + specificity-1 based on the coordinate point value corresponding to the ROC curve.
In the test evaluation, ROC analysis showed that CFT-S identified PCA and AD with area under the curve (AUC) and 95% confidence interval (95% ci) of 0.932 (0.870-0.994) and overlay test AUC and 95% ci of 0.823 (0.717-0.929). ROC analysis also showed that CFT-S identified the area under the curve (AUC) and 95% confidence interval (95% ci) for PCA versus healthy humans as 0.979 (0.947-1.000) and the AUC and 95% ci for overlay test as 0.979 (0.933-1.000). Sensitivity and specificity for identifying PCA and AD were 93.1% and 100.0% when the cut-off of CFT-S was 3.5, respectively.
As an alternative embodiment, the method for correcting the scale based on the evaluation result includes:
and according to the effectiveness analysis, if the Pearson correlation coefficient of a certain local test question score and all local test questions score of the commender is smaller than a set threshold value or the Pearson correlation coefficient of a certain overall test question score and all overall test questions score of the commender is smaller than a set threshold value, deleting the local test questions or the overall test questions, and deleting the other 2 local test questions or the overall test questions using the same picture with the local test questions or the overall test questions.
The embodiment provides a technical scheme for correcting the scale. The embodiment determines whether the test questions in the scale need to be corrected based on the validity analysis result. Specifically, the Pearson correlation coefficient of a certain local test question score (or a certain overall test question score) and all local test question scores (or all overall test question scores) is compared with a set threshold value, and if the correlation coefficient is smaller than the set threshold value, all test questions using the same picture as the test questions are deleted. The threshold value is empirically set, for example, may be set to 0.4.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The simultaneous failure detection method based on the scale is characterized by comprising the following steps of:
designing and determining a scale comprising a plurality of pictures, each picture being a larger geometric figure composed of a plurality of identical smaller basic geometric figures;
drawing up test questions based on the scale, and adopting the test questions to check patients, wherein the patients are required to respectively identify smaller basic geometric figures and larger geometric figures in the pictures;
and obtaining the assessment score of the patient, and obtaining the quantitative evaluation of the severity of the simultaneous disbelief of the patient.
2. The gauge-based simultaneous loss of identity detection method of claim 1, wherein the method of determining the gauge comprises:
designing a plurality of scales to be selected;
transmitting the plurality of candidate scales to an expert group of neurological and psychological experts for voting;
and determining a scale according to the voting result.
3. The gauge-based simultaneous plausibility detection method according to claim 1, wherein the smaller basic geometric figures include regular triangles, squares and circles; the larger geometric figures also include regular triangles, squares and circles, and can be larger solid geometric figures which are fully populated with the smaller basic geometric figures, or larger hollow geometric figures which are only marginally populated with the smaller basic geometric figures.
4. The simultaneous loss of identity detection method of claim 3, wherein the test questions comprise a local test question and a global test question: the local test questions require that smaller basic geometric figures are identified and used for detecting the identification capability of a patient on the shape of a single object; the overall test question requires that a larger geometry, consisting of a smaller basic geometry, be identified for detecting the patient's ability to identify the overall shape.
5. The simultaneous loss of identity detection method based on a scale of claim 4, wherein the test questions comprise 18 different scale pictures, each picture being 3 in number and 54 in total; each picture is used as 1 local test question and 1 whole test question, and 54 local test questions and 54 whole test questions are obtained in total; each test question answering pair is given a score of 1, and the wrong answer does not score.
6. The gauge-based simultaneous plausibility detection method according to claim 5, wherein if a smaller basic geometry or a larger geometry is not recognized within a set time, the corresponding test question is not scored.
7. The scale-based simultaneous plausibility detection method according to claim 5, wherein the patient's assessment score is equal to the difference between the local test question score and the overall test question score; the higher the assessment score, the more serious the malaise.
8. The gauge-based simultaneous loss of identity detection method of claim 1, further comprising: and evaluating the scale by carrying out statistical calculation on the assessment score of the parameter evaluation personnel, and correcting the scale based on the evaluation result.
9. The meter-based simultaneous loss of identity detection method of claim 8, wherein the method of evaluating the meter comprises:
determining a panelist participating in a scale assessment, the panelist comprising a posterior cortical atrophy patient and an alzheimer patient, and a cognitive normative person age-matched to the patient, having no family history of dementia or mental illness;
adopting the test questions to check the reference and evaluation personnel, and carrying out reliability analysis, effectiveness analysis, optimal cut-off point and ROC curve analysis on the scale based on the check scores, wherein the method specifically comprises the following steps of:
verifying the internal consistency of the scale by using Cronbach's alpha coefficient;
re-testing reliability and inter-testing personnel reliability by adopting a correlation coefficient evaluation scale;
content effectiveness of a correlation coefficient evaluation scale is adopted;
determining the feasibility of factor analysis by adopting a KMO test and a Bartlett's test; adopting principal component analysis and a maximum variance rotation test scale to obtain structural effectiveness;
the Pearson correlation coefficient is adopted to evaluate the correlation between the effective mark degree and the overall test and the scale score;
drawing a working characteristic curve of a parametrier, namely an ROC curve, analyzing the sensitivity and the specificity of the scale, and determining the intercept point of the scale.
10. The method of claim 9, wherein the method of correcting the scale based on the evaluation result comprises:
and according to the effectiveness analysis, if the Pearson correlation coefficient of a certain local test question score and all local test questions score of the commender is smaller than a set threshold value or the Pearson correlation coefficient of a certain overall test question score and all overall test questions score of the commender is smaller than a set threshold value, deleting the local test questions or the overall test questions, and deleting the other 2 local test questions or the overall test questions using the same picture with the local test questions or the overall test questions.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU170358U1 (en) * | 2016-12-29 | 2017-04-24 | Леонид Евгеньевич Селявко | Device-simulator for classes on restoration of visual sequential images in patients of a neurological clinic with visual subject agnosia |
| RU2016108203A (en) * | 2016-03-10 | 2017-09-14 | Федеральное унитарное предприятие "Научно-исследовательский институт гигиены, профпатологии и экологии человека" Федерального медико-биологического агентства | Device for determining the characteristics of human attention |
| JP2017217052A (en) * | 2016-06-03 | 2017-12-14 | 一生 重松 | Dementia diagnosis support apparatus, operation method and operation program thereof, and dementia diagnosis support system |
| CN108937969A (en) * | 2018-06-06 | 2018-12-07 | 姜涵予 | A kind of method and device for evaluating and testing cognitive state |
| CN109801523A (en) * | 2019-04-01 | 2019-05-24 | 盐城工学院 | A kind of cognitive training teaching aid |
| WO2020188469A1 (en) * | 2019-03-15 | 2020-09-24 | Neuroinova, Lda | Set and method for screening cognitive impairment |
| RU2779356C1 (en) * | 2021-09-28 | 2022-09-06 | федеральное государственное бюджетное образовательное учреждение высшего образования «Оренбургский государственный университет» | Board game for rehabilitation and development of visual image perception in people diagnosed with simultaneous agnosia |
-
2023
- 2023-06-08 CN CN202310676490.6A patent/CN116807403B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2016108203A (en) * | 2016-03-10 | 2017-09-14 | Федеральное унитарное предприятие "Научно-исследовательский институт гигиены, профпатологии и экологии человека" Федерального медико-биологического агентства | Device for determining the characteristics of human attention |
| JP2017217052A (en) * | 2016-06-03 | 2017-12-14 | 一生 重松 | Dementia diagnosis support apparatus, operation method and operation program thereof, and dementia diagnosis support system |
| RU170358U1 (en) * | 2016-12-29 | 2017-04-24 | Леонид Евгеньевич Селявко | Device-simulator for classes on restoration of visual sequential images in patients of a neurological clinic with visual subject agnosia |
| CN108937969A (en) * | 2018-06-06 | 2018-12-07 | 姜涵予 | A kind of method and device for evaluating and testing cognitive state |
| WO2020188469A1 (en) * | 2019-03-15 | 2020-09-24 | Neuroinova, Lda | Set and method for screening cognitive impairment |
| CN109801523A (en) * | 2019-04-01 | 2019-05-24 | 盐城工学院 | A kind of cognitive training teaching aid |
| RU2779356C1 (en) * | 2021-09-28 | 2022-09-06 | федеральное государственное бюджетное образовательное учреждение высшего образования «Оренбургский государственный университет» | Board game for rehabilitation and development of visual image perception in people diagnosed with simultaneous agnosia |
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