Goal Management Training of Executive Functions in Patients with Spina Bifida: A Randomized Controlled Trial
Bibliographic record
Abstract
Executive dysfunction causes significant real-life disability for patients with spina bifida (SB). However, no previous research has been directed toward the amelioration of executive functioning deficits amongst persons with SB. Goal Management Training (GMT) is a compensatory cognitive rehabilitation approach, addressing underlying deficits in sustained attention to improve executive function. GMT has received empirical support in studies of other patient groups. The purpose of the present study was to determine the efficacy of GMT in treating subjects with SB, using inpatient intervention periods. We hypothesized post-intervention changes in scores on neuropsychological measures to reflect improved attentional control, including sustained attention and inhibitory control. Thirty-eight adult subjects with SB were included in this randomized controlled trial. Inclusion was based upon the presence of executive functioning complaints. Experimental subjects (n = 24) received 21 hr of GMT, with efficacy of GMT being compared to results of subjects in a wait-list condition (n = 14). All subjects were assessed at baseline, post-intervention, and at 6-month follow-up. Findings indicated superior effects of GMT on domain-specific neuropsychological measures and on a functional "real-life" measure, all lasting at least 6 months post-treatment. These results show that deficits in executive functioning can be ameliorated in patients with congenital brain dysfunction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".