Bibliographic record
Abstract
Despite the availability of >25 antiepileptic drugs, 30% of people with epilepsy do not respond to conventional agents, exhibiting pharmacoresistance (PR)--a high percentage that has not changed significantly in decades. This is not surprising given that all current pharmaceutical agents merely reduce the incidence of seizures ("antiictogenic"); they do not interfere with the natural history of epilepsy and are therefore not antiepileptogenic. The two prevailing hypotheses of pharmacoresistance, the target hypothesis and the transporter hypothesis, can only partially explain the complexity and the diversity of PR. It is a neuropharmacologic priority that we change our approach to understanding PR. Herein we suggest the need to regard PR as a complex puzzle in which the target and transporter hypotheses represent a very small piece of the whole. Indeed, we do not even know if this piece of the whole is epiphenomenal or neurobiologically causal. To grasp the whole, we need to constantly gather information (look around for other pieces) from other perspectives and insights coming from clinical, epidemiologic, neuroradiologic, genetic, and other data. A recent research workshop on PR, the 2nd Halifax International Epilepsy Conference & Retreat, chose this eclectic and all-encompassing approach. The participants were fully aware that their diverse contributions represent only still-fragmented pieces of this frustrating but clinically important puzzle.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".