Bibliographic record
Abstract
PURPOSE: This article discusses the conduct of a human abuse potential study as outlined in the Food and Drug Administration Final Guidance to Industry on Assessment of Abuse Potential. In addition, areas where alternative approaches should be considered are proposed. PROCEDURES: The design, end points, conduct, and interpretation of the human abuse potential study were reviewed, analyzed, and placed in the context of current scientific knowledge and best practices to mitigate regulatory risk and expedite drug development. FINDINGS: The guidance is based on regulatory needs and current scientific practices. However, the reliability and utility of such studies can be improved with better subject selection, data collection, standardization of data collection and staff training, and a better understanding of the measurement properties of the dependent measures. CONCLUSIONS: The guidance provides a useful framework for conduct of human abuse potential studies. However, design assumptions, poor choice of end points, failure to consider alternate approaches, and limited experience with interpretation can result in an inadequate study or one that does not fairly represent the abuse potential of a new chemical entity. Methodologic development is needed to strengthen the regulatory framework. The Food and Drug Administration or the National Institutes on Drug Abuse could take a targeted initiative to encourage this work.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".