The diagnostic accuracy of screening questionnaires for the identification of adults with epilepsy: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To describe the diagnostic accuracy of screening questionnaires to identify epilepsy in adults, we performed a systematic review of diagnostic studies that assessed the sensitivity and specificity of such screening questionnaires as compared to a physician's clinical assessment. METHODS: We searched Ovid MEDLINE (1946 to present) and Ovid EMBASE (1947 to present) for studies that estimated the sensitivity and specificity of nonphysician administered screening questionnaires for adults with epilepsy. Both telephone and in-person administered questionnaires were included, whether applied to population or hospital/clinic-based cohorts. The risk of bias was assessed using the Quality Assessment of Diagnostic Studies-2 (QUADAS-2) tool. RESULTS: Our initial search strategy resulted in 917 records. We found nine studies eligible for inclusion. The estimated sensitivity and specificity of the questionnaires used to identify persons with a lifetime history of epilepsy ranged from 81.5% to 100% and 65.6% to 99.2%, respectively. The sensitivity and specificity of these questionnaires in identifying persons with active epilepsy ranged from 48.6% to 100% and 73.9% to 99.9%, respectively. Overall we found a high risk of bias in patient selection and study flow in the majority of studies. SIGNIFICANCE: We identified nine validation studies of epilepsy screening questionnaires, summarized their study characteristics, presented their results, and performed a rigorous quality assessment. This review serves as a basis for future studies by providing a systematic review of existing work. Future research addressing previous limitations will ultimately allow us to more accurately estimate the burden and risk of epilepsy in the general population.
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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 | 0.032 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".