Normalization of deviance in neuromodulation for epilepsy
Bibliographic record
Abstract
The rapid expansion of neuromodulation therapies for drug resistant epilepsy has introduced a growing risk of normalization of deviance (NoD). This occurs when incremental deviations from established procedures are iteratively reinterpreted as the care standard in the absence of immediate adverse outcomes. While motivated by an urgent need to develop novel therapies to improve the quality-of-life of persons with epilepsy, NoD risks harm to the very individuals these neurotechnologies are designed to help. In this commentary, we borrow from organizational safety research to highlight systemic, institutional, and cultural factors that enable NoD in the pursuit of neuromodulation for epilepsy. We highlight the dual imperative of driving forward innovations in neurotechnologies while prioritizing patient safety. Finally, we propose safeguards against NoD including institutional review, centralized data collection, and cultural shifts that prioritize safety and mitigate risk.
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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 | 0.019 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".