P.034 Cost-effectiveness of treatment strategies for medically refractory pediatric epilepsy: a systematic review
Bibliographic record
Abstract
Background: Medically refractory pediatric epilepsy is a disorder that can cause significant financial and physical burden. Although multiple treatments exist, cost-effectiveness remains unclear. We conducted a systematic review to assess cost-effectiveness of treatments for medically refractory pediatric epilepsy and to summarize key issues and areas for further inquiry. Methods: We searched MEDLINE and 6 other databases up to July 2022. We included partial and full economic evaluations (EEs) on treatments for medically refractory pediatric epilepsy. Pairs of reviewers independently screened the literature, extracted data, and assessed quality using the 24-item Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. We extracted data on study characteristics, health outcomes, model design, costs, and treatment characteristics. Results: We identified 37 eligible studies for analysis, 19 of which were partial EEs and 18 were full EEs. Study quality, outcomes reported, treatment comparators, and factors included in cost calculations were common influential factors in study results. Vagus nerve stimulation and cannabinoid oil were the most consistently cost-effective, in 6 of 7 and 1 of 2 studies, respectively. Other treatments were inconsistently cost-effective. Conclusions: The cost-effectiveness of treatments for medically refractory pediatric epilepsy was not definitive. Consistency in study design and inputs is necessary for future comparison of epilepsy treatment.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".