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Multidisciplinary Perspectives on Impaired Control over Substance Use

2006· article· en· W2162305271 on OpenAlexaff
Christopher S. Martin, Mark T. Fillmore, Tammy Chung, Craig Easdon, Klaus A. Miczek

Bibliographic record

VenueAlcoholism Clinical and Experimental Research · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersNational Institute on Drug AbuseNational Institute on Alcohol Abuse and Alcoholism
KeywordsConstruct (python library)Multidisciplinary approachPsychologyControl (management)Substance useMedicineClinical psychologyComputer scienceSociologyArtificial intelligenceSocial science

Abstract

fetched live from OpenAlex

This article presents the proceedings of a symposium held at the June 2005 meeting of the Research Society on Alcoholism in Santa Barbara, California. Impaired control over substance use has long been considered a central feature of alcohol and drug dependence. However, much remains to be learned about the nature of impaired control, the mechanisms by which acute and chronic substance use can lead to impaired control, and how this construct is best assessed in the laboratory and the clinic. The goal of this symposium was to describe current perspectives on impaired control over alcohol and drug use from diverse research areas, to promote future multidisciplinary work in this area. Four speakers described their work on impaired control using human clinical samples (Dr. Chung), animal models (Dr. Miczek), experimental laboratory paradigms in humans (Dr. Fillmore), and neuroimaging studies (Dr. Easdon). Taken together, the talks highlighted the heterogeneous nature of constructs such as impaired inhibitory control, and patterns of impulsive and compulsive substance use. Future clinical and experimental research should attempt to carefully define and measure particular aspects of impaired control and to seek insights from other disciplines.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.206
GPT teacher head0.471
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations31
Published2006
Admission routes1
Has abstractyes

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