Cognitive-physical-functional correlates in chronic brain injury: a pilot study
Bibliographic record
Abstract
Abstract Introduction Functional challenges persist even years following brain injury. Integrating multiple domains as part of therapy may improve global outcomes. The purpose of this study is to investigate the relationships among cognitive, physical and functional domains in adults with chronic brain injury. Material and methods Seventeen community-dwelling brain injury survivors (Stroke n = 8, TBI n = 9) aged 20–60 years, long-term post-brain injury participated in the study. Cognition, including attention, memory, and executive functioning, were examined by select measures of Woodcock-Johnson tests; physical abilities were determined based on muscle strength, gait and balance; functionality was measured based on self-reported questionnaires: community-integration, activities of daily living (ADLs), and satisfaction with life. Results The relationships between cognitive, physical and functional domains were evaluated using Spearman's nonparametric correlational analyses. The physical domains of balance and mobility correlated positively with the cognitive domains of visual-auditory learning (r = 0.90, p = 0.037), as well as with the functional domain scores for Satisfaction with Life (r = 0.671, p = 0.048). Similarly, the productivity subscale of the Community Integration measure was significantly associated with the cognitive domain of concept formation (r = 0.676, p = 0.032). Higher scores on the productivity subscale were moderately related to higher memory scores (r = 0.588) and fluency (r = 0.531). Conclusions The relationships between physical, cognitive, and functional domains could be exploited in long-term periods of recovery following a brain injury. Engagement of one domain to help improve another domain could enhance rehabilitation outcomes. More research is needed to explore the feasibility and benefits of integrative therapies.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".