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Record W2052897003 · doi:10.1037/1064-1297.12.1.20

Disentangling the Sources of Opioid Withdrawal Responses: Comment on McDonald and Siegel (2004).

2004· letter· en· W2052897003 on OpenAlexaff
Jane Stewart

Bibliographic record

VenueExperimental and Clinical Psychopharmacology · 2004
Typeletter
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsConcordia University
Fundersnot available
KeywordsOpioidPsychologyDrug withdrawalDrug toleranceDrugMedicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

R. V. McDonald and S. Siegel (see record 2004-10475-001) present new evidence for the idea that opioid drug-opposite responses can become conditioned to cues of initial drug onset and that they could, therefore, play a role in the development of tolerance of some drug effects and a role in the elicitation of withdrawal-like symptoms in cases in which addicted individuals are exposed to small doses of the drug they normally consume. In this comment, some puzzling features of the data are discussed, and alternative explanations are suggested.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.050
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0020.004
Open science0.0040.002
Research integrity0.0500.041
Insufficient payload (model declined to judge)0.0040.006

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 head0.048
GPT teacher head0.397
Teacher spread0.349 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations3
Published2004
Admission routes1
Has abstractyes

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