The Montreal Cognitive Assessment as a predictor of dropout from residential substance use disorder treatment
Bibliographic record
Abstract
BACKGROUND: Cognitive function is a challenge for many SUD patients, and residential SUD treatment is cognitively demanding. Treatment retention is a predictor for success in SUD treatment, and the literature links low cognitive function to increased dropout rates. In our study we investigate cognitive function and dropout in a residential SUD treatment setting, also accounting for psychological distress. METHODS: We screened a cohort (N = 142) of inpatients for cognitive function (MoCA®) and psychological distress (SCL-10) and calculated the relative risk for dropping out if over versus under the respective cut-off values (<26 and >1.85), and sex, and age-group (<23 years). We also employed a logistic regression with dropout as outcome and MoCA- and SCL-10 scores, and age and days before testing as input. RESULTS: Dropout risk was higher (RR = 1.70) if scoring below MoCA cut-off, and for those younger than 23 years (RR = 2.36). The other variables did not influence dropout risk. MoCA raw scores, age, and SCL-10 were associated with dropout (p < .05); with lower symptoms of psychological distress predicting increased dropout. The interaction between MoCA and SCL-10 scores was not significant (p = .26). CONCLUSIONS: SUD patients should routinely be screened for cognitive impairment, as it predicts dropout. Screenings should be ensued by appropriate adaptations to treatment and further assessment. The MoCA is a useful screening tool for this, independent of psychological distress. Future studies should replicate our findings, investigate specific interventions, and establish SUD population norms for the MoCA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".