Motor impairment rehabilitation post acquired brain injury
Bibliographic record
Abstract
OBJECTIVE: We sought to investigate the efficacy of treatment strategies used to manage motor impairments following acquired brain injury (ABI) in order to provide guidance for clinical practice based on the best available evidence. METHODS AND MAIN OUTCOMES: A systematic review of the literature from 1980-2005 was conducted focusing on pharmacological, non-pharmacological, and exercise interventions available for motor impairments post ABI. The efficacy of a given intervention was classified as strong (supported by two or more randomized controlled trials (RCTs)), moderate (supported by a single RCT), or limited (supported by other types of studies in the absence of RCTs). RESULTS: Thirty-six studies examining a variety of treatment approaches for motor impairments and activity limitations following ABI were evaluated. The majority of interventions are only supported by limited evidence. However, there is strong evidence that serial casting does reduce ankle plantar contractures due to spasticity of cerebral origin, and strong evidence also suggests that partial body weight supported gait training does not provide any added benefit over conventional gait training. There is also moderate evidence to support the use of functional fine motor control retraining to improve motor coordination, tizanidine for upper and lower extremity spasticity, and specific sit-to-stand training to improve functional ability. There is also moderate evidence that casting alone is as effective as casting and Botulinum toxin injections for plantar contractures. CONCLUSIONS: Although there are a variety of treatment strategies to manage motor impairments and activity limitations following ABI, most are only supported by limited evidence pointing to the need for studies of improved methodological quality in this area.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".