C-75 * Neuropsychological Predictors of Outcome in a Substance Abuse Accountability Court Population
Bibliographic record
Abstract
Objective: To determine if neuropsychological variables could be used as predictors of outcome in a substance abuse sample. Method: 61 participants (80% male, 76.9% African American) in their treatment program were selected by the Fulton County Accountability Court in Atlanta, GA to undergo neuropsychological assessment. All met criteria for Substance Abuse dependence. Participants had to be abstinent from all substances of abuse while in the program. The battery given to all participants included the Montreal Cognitive Assessment, Kaufmann-Brief Intelligence Test II, Wide Range Achievement Test-Revision 4, Rey-Osterrieth Complex Figure Test, Copy Phase, Finger Tapping Test, Hand Dynamometer, Trail Making Test (TMT) A & B, Beck Depression Inventory II and Beck Anxiety Inventory. The outcome variables studied were whether the participants graduated or were terminated from the program, and length of time in the program. Results: Among the portion of the sample who were terminated from the program, education level, R2 = .131, F(1, 34) = 5.136, p < .05; Trails A z-score R2 = .608, F(1, 8) = 12.409, p < .01; Trails A time, R2 = .693, F(1, 8) = 18.079, p < .05; Trails B z-score R2 = .561, F(1, 8) = 10.211, p < .05; and Trails B time, R2 = .649, F(1, 7) = 712.946, p < .01, predicted length in the program. Education was a significant moderator of this relationship. Conclusion(s): Performance on the TMT appears to predict time spent in the program and this effect is moderated by education level. A discussion of the cognitively mediated neuropsychological processes underlying successful performance on the TMT will be provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".