Rehabilitation of Executive Functioning in Patients with Frontal Lobe Brain Damage with Goal Management Training
Bibliographic record
Abstract
Executive functioning deficits due to brain disease affecting frontal lobe functions cause significant real-life disability, yet solid evidence in support of executive functioning interventions is lacking. Goal Management Training (GMT), an executive functioning intervention that draws upon theories concerning goal processing and sustained attention, has received empirical support in studies of patients with traumatic brain injury, normal aging, and case studies. GMT promotes a mindful approach to complex real-life tasks that pose problems for patients with executive functioning deficits, with a main goal of periodically stopping ongoing behavior to monitor and adjust goals. In this controlled trial, an expanded version of GMT was compared to an alternative intervention, Brain Health Workshop that was matched to GMT on non-specific characteristics that can affect intervention outcome. Participants included 19 individuals in the chronic phase of recovery from brain disease (predominantly stroke) affecting frontal lobe function. Outcome data indicated specific effects of GMT on the Sustained Attention to Response Task as well as the Tower Test, a visuospatial problem-solving measure that reflected far transfer of training effects. There were no significant effects on self-report questionnaires, likely owing to the complexity of these measures in this heterogeneous patient sample. Overall, these data support the efficacy of GMT in the rehabilitation of executive functioning deficits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".