Medication Versus Management: Beyond Pills-to-Mouths Measures of Epilepsy Care (I14.003)
Bibliographic record
Abstract
Objective: To introduce the concept of the epilepsy management gap. Background: The treatment gap implies that administering antiepileptic drugs (AEDs) is a sufficient measure epilepsy care. We propose the epilepsy management gap concept to capture PWE who may take AEDs but continue to incur risk or experience suboptimal management. Methods: PWE in Bhutan (National Referral Hospital, 2014-2015) completed a clinical questionnaire, the Quality of Life in Epilepsy inventory (QOLIE-31), and an electroencephalogram (EEG). Management gap was the proportion of participants meeting six pre-specified criteria based on known best practices in epilepsy care and the United Kingdom’s NICE guidelines. Results: Among 253 participants (53[percnt] female; median 24 years), 93[percnt](n=235) were treated with AEDs. 72[percnt](n=183) of the participants had active epilepsy (seizure in prior year) and 36[percnt](n=92) had epileptiform abnormalities on EEG. At least one criterion was met by 55[percnt](n=138) of participants, whereas treatment gap encompassed only 5[percnt](n=13) of participants. Criteria 1. Among 18 participants taking no AED, 72[percnt](n=13) had active epilepsy. 2. Among 26 adult participants on subtheraputic monotherapy, 46[percnt](n=12) had active epilepsy. 3. Among 67 participants reporting unintentional seizure-related injuries, 87[percnt](n=58) had active epilepsy. 4. Among 111 participants with a QOLIE-31 score below 50/100, 77[percnt](n=86) had active epilepsy. 5. Among 48 participants reporting absence seizures, 56[percnt](n=27) were treated with carbamazepine or phenytoin. 6. Among 101 female participants aged 14-40 years, 23[percnt](n=23) were treated with sodium valproate. Two management gap criteria were met by 16[percnt](n=40) of participants, three were met by 6[percnt](n=16), and 1[percnt](n=3) met four criteria. Epileptiform discharges on EEG (odds ratio 1.95, 95[percnt]CI 1.15, 3.29) and years since first AED treatment (odds ratio 1.07, 95[percnt]CI 1.03, 1.12) were significantly associated with more criteria met. Conclusions: By defining the management gap, patient subpopulations at greatest need for targeted epilepsy care interventions may be prioritized. Support: Grand Challenges Canada, Thrasher Fund.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".