Novel Approaches in Assessing Abuse Potential of Psychotropic Medications - Pregabalin as a Test Case
Bibliographic record
Abstract
Abuse of psychotropic medications causes significant morbidity and mortality in North America. Results from traditional assessments of abuse liability may not be generalizable to individuals with mental illnesses or for drugs with ill-defined mechanisms of action. Pregabalin is an anticonvulsant medication used off-label to manage benzodiazepine withdrawal symptoms. Simultaneously, reports of its abuse have emerged. Two novel approaches were used to assess pregabalin’s abuse potential. On the macro-level, adverse drug reaction data from Canada’s national database were evaluated in Study 1. Results indicated that cases found in this spontaneous reporting system can provide early indicators of prescription drug abuse. On the micro-level, Study 2 aimed to assess the acute effects of pregabalin in inpatients withdrawing from benzodiazepines and produced valuable information concerning study design, which can be used to inform future research. Overall, these novel macro-and micro-level approaches in assessing abuse potential of psychotropic drugs are complementary and synergistic with traditional methods.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".