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Record W3195786982 · doi:10.1080/14659891.2021.1967487

Difference between psychostimulant users and opioid users in recovery of cognitive impairment, measured with the Montreal Cognitive Assessment (MoCA®)

2021· article· en· W3195786982 on OpenAlexaboutno aff
Hilde Hoel Fjærli, Mikael Julius Sømhovd, Tone H. Bergly

Bibliographic record

VenueJournal of Substance Use · 2021
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionAbstinenceOpioidPsychologyCognitive impairmentOpiatePsychiatryClinical psychologyMedicineAudiologyInternal medicine

Abstract

fetched live from OpenAlex

Objective Substance use disorder (SUD) can lead to cognitive impairment. The objective of our study is to investigate differences in cognitive impairment between psychostimulant users and opioid users after long-term abstinence. Specifically, we expected patients with a mainly high-frequency use of psychostimulants to show less improvement on the Montreal Cognitive Assessment (MoCA®) screening.Methods The overall analyses include patients (N = 91) having MoCA® scores from both the time of admission and before discharge from long-term treatment. The studied subgroups comprised 21 participants each.Results The MoCA® sum-scores were statistically equal in the groups both at admission and at discharge. Within-subjects t-tests of the sum-score suggest a significant change from admission to discharge for the opiate group, but not for the psychostimulant group. The psychostimulant users were also tested later than the opioid users.Conclusion Our data indicate that there may be differences in how mainly psychostimulant-using patients recover in cognitive functioning compared to patients mainly using opioids. There should be a heightened focus on cognitive function, and adaptation of the treatment content may be warranted for patients with mainly psychostimulant use.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

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

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 head0.028
GPT teacher head0.296
Teacher spread0.267 · 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 designObservational
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

Citations5
Published2021
Admission routes1
Has abstractyes

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