The primary exploration in clinical value of the CLSA ⁃ EA questionnaire (Chinese version) in adult epilepsy screening
Bibliographic record
Abstract
Objective To evaluate the reliability, validity and diagnostic accuracy of the Canadian Longitudinal Study on Aging Epilepsy Algorithm (CLSA-EA) questionnaire (Chinese version) in screening Chinese adult epilepsy. Methods From August to October 2022, a total of 384 subjects and patients were included, who were respectively screened from urban and rural communities in Chengdu, Sichuan, and Department of Neurology of West China Hospital, Sichuan University. The CLSA - EA questionnaire (Chinese version) was used for the survey. Epilepsy was diagnosed according to 2014 International League Against Epilepsy (ILAE) criteria. Kappa coefficient and Cronbach's α coefficient were used to evaluate testretest reliability and internal consistency, respectively. Construct validity was used to test validity. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value and diagnostic consistency κ value were calculated by confusion matrix method to evaluate the effect of screening. Results Finally, 348 questionnaires were obtained, including 190 epileptic cases (54.60%) and 158 nonepileptic cases (45.40%), of which 244 were retested subjects, including 123 epileptic cases (50.41%) and 121 non-epileptic cases (49.59%). Retest reliability analysis in epileptic cases showed that the consistency of items Q3a, Q3bⅲ , Q3bⅳ , Q4a and Q4b in CLSA-EA questionnaire (Chinese version) had moderate consistency (0.400 ≤ κ < 0.750; P = 0.000, for all). Items Q1, Q2, Q3bⅰ, Q3bⅱ, Q3bⅴ, Q3bⅵ and Q3bⅶ had poor consistency (0.000 ≤ κ < 0.400; P < 0.05, for all). In non-epileptic cases, the consistencies of items Q2, Q3a, Q3bⅰ, Q4a and Q4b were moderate (P = 0.000, for all), Q3bⅴ and Q3bⅵ were poor (P = 0.000, for all), and no consistencies of Q3bⅱ , Q3bⅲ and Q3bⅳ were found (P > 0.05, for all). The internal consistency evaluation in epileptic cases showed that the internal consistency of all items and the sum of all items in the CLSA-EA questionnaire (Chinese version) were poor (Cronbach's α coefficient < 0.700). The internal consistency of items Q4a and Q4b for non-epileptic patients was also poor, but other items and the sum of all items were acceptable (Cronbach's α coefficient 0.700-0.800). The structural validity analysis showed that the cumulative variance contribution rate of the four factors was only 43%, and items Q1 and Q3a were not incorporated into the factors. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value and consistency of CLSA -EA2 epilepsy determination algorithm were higher (> 0.900, for all). Conclusions The CLSA - EA questionnaire (Chinese version) has high sensitivity and specificity, which can guide the preliminary screening of epilepsy in China. However, the applicability, reliability and validity still need to be further considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".