COGNITIVE IMPAIRMENTS AND THE EFFICACY OF PSYCHOCORRECTIONAL AND REHABILITATION INTERVENTIONS IN PATIENTS UNDERGOING OPIOID AGONIST MAINTENANCE THERAPY
Bibliographic record
Abstract
Opioid dependence remains a significant medical and social issue, with opioid agonist maintenance therapy (OAMT) using methadone being the primary treatment approach in Ukraine. However, the potential impact of methadone on cognitive functioning continues is a matter of scientific debate. This study aimed to evaluate the cognitive outcomes of OAMT and the effectiveness of psychocorrective and rehabilitation interventions in patients with opioid dependence. A total of 150 patients were examined and divided into two groups: the main group (n = 101; OAMT + psychotherapeutic and psychocorrective interventions) and the comparison group (n = 49; OAMT only). Cognitive functioning was assessed using the MoCA scale, the SCL‑90-R questionnaire, and the WHOQOL-BREF. At baseline, mild cognitive impairment was observed in 54.67 % of participants, more frequently in the comparison group. After intervention, the proportion of patients without cognitive deficits significantly increased in the main group (59.3 % vs. 15.8 % in the comparison group), while mild impairment predominated in the control group (81.6 %). After the intervention, MoCA scores in the main group were 26.0 [25.0; 27.0] points, and in the comparison group — 24.0 [23.0; 25.0] points, i.e. there was a decrease in the variability of the results in both groups, with a greater concentration of high scores in patients receiving multicomponent treatment. The between-group difference remained statistically significant (p< 0.05). The calculated Cohen’s d (~0.5) indicates a moderate effect size of the psychocorrective intervention. These findings demonstrate the positive impact of a multicomponent treatment approach that combines OAMT with psychotherapeutic programs, contributing to improved cognitive functioning and enhancing the effectiveness of patient resocialization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".