Effects of Cognitive Impairment on Substance Abuse Treatment Attendance: Predictive Validation of a Brief Cognitive Screening Measure
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Neuropsychological impairment among patients with substance use disorders (SUDs) contributes to poorer treatment processes and outcomes. However, neuropsychological assessment is typically not an aspect of patient evaluation in SUD treatment programs because it is prohibitively time and resource consuming. In a previous study, we examined the concurrent validity, classification accuracy, and clinical utility of a brief screening measure, the Montreal Cognitive Assessment (MoCA), in identifying cognitive impairment among SUD patients. To provide further evidence of criterion-related validity, MoCA classification should optimally predict a clinically relevant behavior or outcome among SUD patients. The purpose of this study was to examine the validity of the MoCA in predicting treatment attendance. METHODS: We compared previously collected clinical assessment data on 60 SUD patients receiving treatment in a program of short duration and high intensity to attendance data obtained via medical chart review. RESULTS: Though the proportion of therapy sessions attended did not differ between groups, cognitively impaired subjects were significantly less likely than unimpaired subjects to attend all of their group therapy sessions. CONCLUSION: These results complement our previous findings by providing further evidence of criterion-related validity of the MoCA in predicting a clinically relevant behavior (i.e., perfect attendance) among SUD patients. SCIENTIFIC SIGNIFICANCE: The capacity of the MoCA to predict a clinically relevant behavior provides support for its validity as a brief cognitive screening measure.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".