Difference between psychostimulant users and opioid users in recovery of cognitive impairment, measured with the Montreal Cognitive Assessment (MoCA®)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".