Identification of Optimal Measures of Human Abuse Potential
Bibliographic record
Abstract
BACKGROUND: Human abuse potential studies include multiple measures to assess the subjective effects of central nervous system-active drugs. In this retrospective analysis, measurement properties of commonly used measures were assessed, and factor analysis was conducted to identify a core battery of measures. METHODS: Measures of positive, negative and other effects, for example, bipolar "at-the-moment" Drug Liking visual analog scale (VAS), were derived for active controls and placebo from 19 studies in recreational drug users (N = 570). Distribution, placebo response, variability, convergent/discriminant validity, parameter effect sizes (eg, maximum effect [Emax], time-averaged area under the effect curve), and predictive validity were evaluated. A factor analysis was conducted with 9 studies. RESULTS: Most parameters were not normally distributed. Bipolar VAS exhibited the lowest variability. Drug Liking VAS Emax was very sensitive, showed large effect sizes (>1.0), and was moderately to strongly correlated with Emax of other positive effects measures (r > 0.5), but weaker with less specific scales (eg, high, Any Effects VAS); time-averaged area under the effect curve showed higher variability and lower effect sizes. Maximum effect at any dose (EmaxD) was significantly correlated with Emax across all selected measures and showed higher effect sizes. In the overall factor analysis, factors could be categorized into positive effects/euphoria (77% of variance), negative effects (17.9%), and pharmacologic effects (5%). For predictive validity, effect sizes for Drug Liking VAS Emax/EmaxD were moderately correlated with postmarket adverse events related to abuse (R = 0.52). CONCLUSIONS: A core battery of 7 subjective measures was proposed, with additional measures added based on pharmacologic effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".