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Record W2312407949 · doi:10.1097/fbp.0b013e328363d1e2

A proactive nonclinical drug abuse and dependence liability assessment strategy

2013· review· en· W2312407949 on OpenAlex
Michael D.B. Swedberg

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBehavioural Pharmacology · 2013
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsnot available
FundersHealth Canada
KeywordsDocumentationLiabilityAbuse liabilityAttritionBusinessRisk analysis (engineering)Process (computing)Substance abuseDrugProcess managementPsychologyActuarial scienceMedicineComputer sciencePsychiatryAccounting

Abstract

fetched live from OpenAlex

This paper outlines a strategy and process for proactive nonclinical assessment of drug abuse and dependence liability of new compounds intended for clinical use. Documentation of the potential for causing abuse and dependence liability is required for registration of a new drug; hence, proactive timing and planning of these studies allows for appropriate documentation of nonclinical as well as clinical data in time for registration. In cases for which an abuse and dependence liability label may not be acceptable, a proactive approach to abuse and dependence liability assessment allows for replacement of selected compounds at an early stage of development, thereby saving time and resources and avoiding late attrition.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.257
GPT teacher head0.532
Teacher spread0.275 · 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