The impact of memory training on abstinence among individuals with alcohol use disorder
Bibliographic record
Abstract
Background: Chronic excessive alcohol consumption is associated with cognitive deficits. Patients with cognitive impairment, particularly memory deficits, may have difficulties in acquiring new semantic and procedural information which could affect the effectiveness of clinical treatments. Memory training (MT) as an adjunct to evidence-based treatments is a promising approach to improve memory, cognitive functions, and abstinence rates. The objective of the study was to determine whether MT could positively influence memory function and long-term abstinence in individuals with alcohol use disorder (AUD) undergoing detoxification. Methods: = 210) and assigned to the control arm (treatment-as-usual only; no-MT) or the experimental arm (treatment-as-usual with MT). At weeks 2, 6, and 10, cognitive function was examined using a comprehensive battery of neuropsychological tests. The abstinence rate was assessed at months 3 and 6 after discharge. Results: Memory performance significantly improved over the course of treatment, among both groups. However, patients who had received MT showed significantly greater improvement and a significantly higher abstinence rate six months after discharge (53%), compared to the no-MT group (36%). Conclusions: Memory training appears to be a promising supplementary therapy for withdrawal treatment of patients with AUD, resulting in improved memory and long-term abstinence. Future research into the effectiveness of cognitive training should be conducted in other treatment settings and for other substance use disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".