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Record W2983830581 · doi:10.1097/jcp.0000000000000838

Improving the Clinical Pharmacologic Assessment of Abuse Potential

2018· review· en· W2983830581 on OpenAlexaff
Edward M. Sellers

Bibliographic record

VenueJournal of Clinical Psychopharmacology · 2018
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsMuscular Dystrophy CanadaUniversity of Toronto
Fundersnot available
KeywordsStandardizationContext (archaeology)Substance abuseRisk analysis (engineering)Food and drug administrationMedicinePsychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.008
Bibliometrics0.0010.001
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.299
GPT teacher head0.626
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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