The identification of persons with epilepsy in a population-based cohort
Bibliographic record
Abstract
Estimating the prevalence and incidence of epilepsy in the absence of physician assessment is challenging. In Canada, the incidence of epilepsy is unknown while two reports of the estimated lifetime prevalence are based on a subject’s response to a single unvalidated question to screen for epilepsy. The Canadian Longitudinal Study of Aging (CLSA) is a nationwide population study of 50,000 people aged 45-85 years at baseline which presently also relies on a similar unvalidated question to identify persons with epilepsy.The purpose of this research is threefold: (a) to systematically review non-physician administered screening tools reported in the scientific literature designed to identify persons with epilepsy (PWE) in population-based cohorts; (b) to design a screening questionnaire and disease-ascertainment algorithm to identify PWE in a population-based cohort; (c) to investigate the performance of this disease-ascertainment algorithm in a consecutive sample of CLSA participants alongside a consecutive sample of individuals from an epilepsy-enriched general neurology clinic at the Montreal Neurological Institute and Hospital.This thesis centres on two manuscripts. The first presents the results of a systematic review of screening questionnaires. In it, we concluded that 10 studies were eligible for inclusion. The estimated sensitivity and specificity of these tools in identifying persons with a lifetime history of epilepsy ranged from 61.5% to 100% and 65.6% to 99.2%, respectively. The sensitivity and specificity of these tools in identifying persons with active epilepsy ranged from 48.6% to 100% and 73.9% to 99.9%, respectively. Overall we found that there was high risk of bias in patient selection, the index test and flow/timing in the majority of studies while six studies used an affected case vs. unaffected control study design creating the potential for spectrum bias and subsequently inflated accuracy estimates.The second manuscript presents the Canadian Longitudinal Study on Aging – Epilepsy Algorithm (CLSA-EA), a questionnaire and disease-ascertainment algorithm, as well as the results of a validation study. In this validation study, we recruited 242 participants, 34 of whom were diagnosed with epilepsy by one of our study neurologists. The sensitivity and specificity of the CLSA-EA for a lifetime history of epilepsy were 97.1% and 98.1%, and for active epilepsy were 100% and 98.6%.It is important from a clinical, epidemiologic and public policy perspective to accurately ascertain the incidence and prevalence of epilepsy in population-based cohorts. After a systematic review of the literature, careful design and final validation to assess its diagnostic accuracy, we present the CLSA-EA. Plans are underway to apply the CLSA-EA to all 50,000 participants in the CLSA cohort with the aim to provide a valid estimate of the incidence and prevalence of epilepsy in those aged greater than 45 years in Canada.
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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 | 0.029 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".