Neuropsychological counseling improves social behavior in cognitively-impaired multiple sclerosis patients
Bibliographic record
Abstract
We studied the effectiveness of a newly-developed cognitive-behavioral intervention in 15 patients with marked cognitive impairment and behavior disorder. The design was a single-blind test of a neuropsychological intervention, with pre- and post-treatment assessments of personality and social behavior. MS patients underwent neurological examination and neuropsychological testing at baseline. The patients were then randomly assigned to neuropsychological counseling or standard, non-specific supportive psychotherapy. The active 12-week treatment emphasized enhancement of insight through education, social skills training, and behavior modification. All patients were re-examined within 2 weeks of the termination of treatment. Neuropsychological technicians were blind to treatment condition. Both groups showed evidence of cognitive impairment and personality/behavior disorder prior to treatment and were well matched on demographic, disability, and cognitive measures. Patients who underwent neuropsychological counseling showed significant positive response on measures of social behavior (e.g. excessive ego-centric speech) compared to those who underwent standard counseling. We conclude that these data support the use of non-pharmacological, neuropsychological counseling in patients with acquired, MS-associated behavior disorder.
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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) | 0.001 | 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".