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Record W2334116347 · doi:10.1097/fbp.0000000000000106

Naloxone-precipitated withdrawal causes an increase in impulsivity in morphine-dependent rats

2014· article· en· W2334116347 on OpenAlexafffund
Colin Harvey-Lewis, Allyson D. Brisebois, Hyunchoong Yong, Keith B.J. Franklin

Bibliographic record

VenueBehavioural Pharmacology · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpulsivity(+)-NaloxoneDelay discountingMorphinePsychologyOpiateMedicineInternal medicineSalineAnesthesiaEndocrinologyAntagonistClinical psychologyReceptor

Abstract

fetched live from OpenAlex

Opiate dependence is associated with increased impulsivity in both humans and animals. Although the state of withdrawal appears to contribute to this effect, a causal relationship has not been shown. Here, we test whether precipitating withdrawal in morphine-dependent rats through naloxone can cause increased impulsivity. Rats were trained on a delay discounting task and then randomly assigned to either a dependent group that received a nightly 30 mg/kg subcutaneous dose of morphine or a naive group that received nightly saline. Once dependence was established, 2-day test delay discounting curves were determined 1 h after three doses of naloxone (0, 0.25, 0.5 mg/kg). In dependent rats both doses of naloxone caused increased preference for the small reward at short delays. Naloxone had no effect on delay discounting in naive rats. We conclude that precipitating withdrawal in dependent rats can cause increased impulsivity.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.330
Teacher spread0.289 · 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 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

Citations17
Published2014
Admission routes2
Has abstractyes

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