CLINICAL CONSEQUENCES OF GENERIC SUBSTITUTION OF LAMOTRIGINE FOR PATIENTS WITH EPILEPSY
Bibliographic record
Abstract
LeLorier et al.1 studied the risks associated with patients switching to and from generic antiepileptic drugs (AEDs) in Quebec. The authors did not consider that such changes could be attributed to promotionally driven doctor and patient preferences. Industry representatives vigorously promote the idea that generics are less potent (“up to 20% less effective”) than their brand name equivalents despite Food and Drug Administration (FDA) assertions to the contrary.2 Study patients taking generics underwent dose escalations. The authors suggest that dose escalations were in response to increased side effects, but this is counterintuitive. More plausibly, anxiety-induced dose escalations contributed to side effects and, in turn, switch-backs. The unspoken hypothesis that switches to generic led to more seizures is unaddressed by the presented data, which blur psychiatric and neurologic indications for lamotrigine (LTG). LTG is used heavily in psychiatry and most recent growth in sales is driven by the psychiatric market.3 A single claim submitted with a code for epilepsy is considered sufficient evidence that LTG is being prescribed as an AED, but this is unlikely. The leading outpatient diagnostic code as well as four of five diagnostic codes for outpatient visits and two of three diagnostic codes for …
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".