Categorization of Abuse Potential–Related Adverse Events
Bibliographic record
Abstract
All drugs with central nervous system activity must undergo an assessment of their abuse potential, and these data must be included in a New Drug Application. Part of this assessment is an analysis of treatment-emergent adverse events that occur during clinical development. Using an iterative consensus strategy, we evaluated and grouped an available list of 213 flag terms for abuse potential from the Food and Drug Administration, into categories and assessed the relevance of the terms to primary abuse behavior. Consequences of abuse (28%) were most common, followed by diagnoses (19%), altered thoughts (18%), cognitive effects (10%), stimulation/anxiety (9%), central nervous system depression (9%), and mood elevation (1%). The vast majority of abuse potential-related terms reflects treatment-emergent adverse events, not behaviorally motivating features to abuse a drug. Almost 30% of terms are related to altered perception or altered cognition. These are serious consequences in the context of abusable psychoactive drugs. Only 20% of terms were rated as definitely or probably reflecting intrinsic behavioral reinforcing potential, and 30% were assessed as having weak predictive utility. Sponsors need to have an explicit strategy for collecting, interpreting, and analyzing abuse-related adverse event information completely and accurately.
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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 | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".