Development and Pilot Implementation of a Theory-Based Cognitive Rehabilitation Protocol for Adults With Chronic Cognitive Complaints After Mild Traumatic Brain Injury
Bibliographic record
Abstract
PURPOSE: The aim of this study was to describe the development of and pilot feasibility outcomes for a strategy-based, brief, intensive cognitive rehabilitation intervention delivered to U.S. service members and veterans with mild traumatic brain injury in a recently completed 3-year pragmatic clinical trial: Symptom-Targeted Approach to Rehabilitation for Concussion (STAR-C). METHOD: To develop STAR-C, we used the Rehabilitation Treatment Specification System to identify core elements and principles from a previous randomized clinical trial of cognitive rehabilitation, and incorporated principles of neuroplasticity (e.g., high-dose spaced practice of personally meaningful tasks), best clinical practices (e.g., client-centered goal setting), health psychology (e.g., a focus on self-efficacy and motivation), and community-based participation research (e.g., the protocol was co-designed by clinicians and researchers). Treatment was based on a resource-allocation theory of everyday cognitive challenges, which predicted that automatic strategy use would reduce cognitive demands of everyday activities and therefore reduce cognitive symptoms. Treatment was delivered by speech-language pathologists (SLPs) and occupational therapists (OTs), using a protocol that included a problem-focused intake questionnaire, manualized treatment, and clinician resources. Therapy was delivered individually in six to 10 virtual or in-person sessions over 3-4 weeks. Therapy focused on desired changes in function, scaled using Goal Attainment Scaling. RESULTS: Trained SLPs and OTs delivered STAR-C to 53 U.S. service members and veterans, with treatment fidelity > 95%. Participants and clinicians rated STAR-C as acceptable, feasible, and appropriate, and most participants attained and maintained targets. CONCLUSION: STAR-C appears to be a feasible method for improving everyday cognitive performance and efficacy should be tested in a controlled study. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.28222613.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".