Evidence map›Paper›PMID 33362606›Full record

ArticleFrontiers in psychiatry2020

Probabilistic Reversal Learning Deficits in Patients With Methamphetamine Use Disorder-A Longitudinal Pilot Study.

Maximilian Pilhatsch, Shakoor Pooseh, Alexandra Junke, Milky Kohno, Johannes Petzold, Cathrin Sauer, Michael N Smolka

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

7 authors.

Maximilian PilhatschDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Shakoor PoosehDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Alexandra JunkeDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Milky KohnoDepartment of Psychiatry, Oregon Health and Science University, Portland, OR, United States.
Johannes PetzoldDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Cathrin SauerDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.
Michael N SmolkaDepartment of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Methamphetamine use disorder (MUD) is increasing worldwide and commonly associated with learning deficits. Little is known the about underlying trajectories, i.e., how the affected higher-order cognitive functions develop over time and with respect to abstinence and relapse. A probabilistic reversal learning (PRL) paradigm was implemented to uncover the microstructure of impulsive choice and maladaptive learning strategies in 23 patients with MUD in comparison with 24 controls. Baseline data revealed fewer optimal choices and a pattern of altered learning behavior from negative and positive feedback in patients suggesting impairments in flexibly-adapting behavior to changes of reward contingencies. Integrating longitudinal data from a follow-up assessment after 3 months of specific treatment revealed a group-by-time interaction indicating a normalization of these cognitive impairments in patients with MUD. In summary, our study demonstrates behavioral correlates of maladaptive decision-making processes in patients with MUD, which may recover after 3 months of MUD-specific therapy paving the way for further learning-based interventions. Limited by a small sample size, the results of this pilot study warrant replication in larger populations.

Indexed as

crystal methlongitudinalmethamphetamineprobabilisticpsychotherapyreversal learning

Identifiers

PMID33362606
PMCPMC7755887

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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.