Increasing the amount of usual rehabilitation improves activity after stroke: a systematic review
Bibliographic record
Abstract
QUESTIONS: In people receiving rehabilitation aimed at reducing activity limitations of the lower and/or upper limb after stroke, does adding extra rehabilitation (of the same content as the usual rehabilitation) improve activity? What is the amount of extra rehabilitation that needs to be provided to achieve a beneficial effect? DESIGN: Systematic review with meta-analysis of randomised trials. PARTICIPANTS: Adults aged 18 years or older that had a diagnosis of stroke. INTERVENTION: Extra rehabilitation with the same content as usual rehabilitation aimed at reducing activity limitations of the lower and/or upper limb. OUTCOME MEASURES: Activity measured as lower or upper limb ability. RESULTS: A total of 14 studies, comprising 15 comparisons, met the inclusion criteria. Pooling data from all the included studies showed that extra rehabilitation improved activity immediately after the intervention period (SMD=0.39, 95% CI 0.07 to 0.71, I(2)=66%). When only studies with a large increase in rehabilitation (> 100%) were included, the effect was greater (SMD 0.59, 95% CI 0.23 to 0.94, I(2)=44%). There was a trend towards a positive relationship (r=0.53, p=0.09) between extra rehabilitation and improved activity. The turning point on the ROC curve of false versus true benefit (AUC=0.88, p=0.04) indicated that at least an extra 240% of rehabilitation was needed for significant likelihood that extra rehabilitation would improve activity. CONCLUSION: Increasing the amount of usual rehabilitation aimed at reducing activity limitations improves activity in people after stroke. The amount of extra rehabilitation that needs to be provided to achieve a beneficial effect is large. TRIAL REGISTRATION: PROSPERO CRD42012003221. [Schneider EJ, Lannin NA, Ada L, Schmidt J (2016) Increasing the amount of usual rehabilitation improves activity after stroke: a systematic review.Journal of Physiotherapy62: 182-187].
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".