Treatment Interventions for the Paretic Upper Limb of Stroke Survivors: A Critical Review
Bibliographic record
Abstract
Despite a threefold increase in treatment interventions studies during the past 10 years, "best practice" for the rehabilitation of the paretic upper limb is still unclear. This review aims to lessen uncertainty in the management of the poststroke upper limb. Two separate searches of the scientific literature from 1966-2001 yielded 333 articles. Three referees, using strict inclusion and exclusion criteria, selected 68 relevant references. Cohort studies, randomized control trials, and systematic reviews were critically appraised. Mean randomized control trial quality (n = 33) was 17.1/27 (SD = 5.2, 95% CI = 15.2-19.0, range = 6-26). Mean quality of cohort studies (n = 29) was 11.8/27 (SD = 3.8, 95% CI = 10.4-13.2, range = 4-19). Quantitative syntheses were done using the Z-statistic. This systematic review indicated that sensorimotor training; motor learning training that includes the use of imagery, electrical stimulation alone, or combined with biofeedback; and engaging the client in repetitive, novel tasks can be effective in reducing motor impairment after stroke. Furthermore, careful handling, electrical stimulation, movement with elevation, strapping, and the avoidance of overhead pulleys could effectively reduce or prevent pain in the paretic upper limb. Rehabilitation specialists can use this research synthesis to guide their selection of effective treatment techniques for persons with impairments after stroke.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".