Evidence map›Paper›PMID 42349660›Full record

ArticleBehavioural brain research2026

Dissociation of decision-making from escalated drug intake within a long-access model.

McAllister Stephens, Joshua Beckmann

Abstract read
In one paragraph

Article in Behavioural brain research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

McAllister StephensCentre College, United States. Electronic address: McAllister.stephens@gmail.com.
Joshua BeckmannUniversity of Kentucky, Lexington, KY, United States. Electronic address: joshua.beckmann@uky.edu.

Funding

A Translational Determination of the Mechanisms of Maladaptive Choice in Opioid Use DisorderR01DA047368 · NIDA · UNIVERSITY OF KENTUCKY · PI BECKMANN, JOSHUA, LILE, JOSHUA ANTHONY · 2019 to 2023
$3.1M
A translational determination of the mechanisms of maladaptive choice in cocaine use disorderR01DA045023 · NIDA · UNIVERSITY OF KENTUCKY · PI BECKMANN, JOSHUA, LILE, JOSHUA ANTHONY · 2018 to 2022
$3.0M
NIDA NIH HHS R01 DA045023NIDA NIH HHS R01 DA047368
6 · The paper itself

Abstract

General decision-making deficits induced by chronic drug intake have been suggested to underlie the development of substance use disorder (SUD). To determine the effects of differential drug self-administration (cocaine or fentanyl) history on decision making, a procedure built upon choice theory was run alongside a drug (cocaine or fentanyl) escalation procedure in rats. Isomorphic food choice was measured each day under unpredictable probabilistic conditions that required constant engagement of decision systems, followed by self-administered drug (cocaine or fentanyl) for 1or 6 h. Despite clear intake escalation (a measure widely used as indication of dysregulated decision-making and posited as a preclinical model of SUD in which animals take increasing amounts of drug compared to their 1-hr access counterparts), isomorphic food choice did not differ between drug access groups during self-administration or subsequent forced abstinence, suggesting that decision-making remained intact and that general drug-induced deficits in decision-making are not necessarily an underlying contributor in intake escalation within the long-access preclinical model of SUD.

Indexed as

CocaineDecision MakingFentanylSubstance-Related DisordersAnimalsChoice BehaviorDisease Models, AnimalMaleRatsSelf AdministrationCocaineFentanylCocaineDecision-MakingEscalationFentanylRelative ValueSubstance Use Disorder (SUD)Value

Identifiers

PMID42349660
PMCPMC13459739

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.