Evidence for Therapeutic Interventions for Hemiplegic Shoulder Pain During the Chronic Stage of Stroke: A Review
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness of therapeutic interventions targeting hemiplegic shoulder pain (HSP) more than 6 months post stroke. METHODS: A literature search of multiple databases (PubMed, CINAHL, Ovid, and EMBASE) was conducted to identify articles published in the English language from 1980 to April 2012. Studies were included if (1) all participants were adults who had sustained a stroke; (2) research design was a randomized controlled trial (RCT) that examined the effectiveness of any treatment for HSP; (3) all participants had experienced stroke at least 6 months previously; and (4) an assessment of pain had been conducted before and after treatment using a standardized method. The following data were extracted: patient characteristics (ie, age, gender, time since stroke), sample size, study design, measurement of pain pre and post treatment, and adverse events. RESULTS: Ten RCTs (PEDro scores 4-9) met inclusion criteria and included a total sample size of 388 individuals with a mean age of 53.2 years (range, 43.6-73.2). Mean time post stroke was 18.4 months. Three studies addressed the use of botulinum toxin type A (BTx-A); 2 studies examined electrical stimulation; 3 studies focused on intraarticular glenohumeral corticosteroid injections; 1 studied subacromial corticosteroid injections; and 1 study looked at massage therapy. CONCLUSIONS: Positive outcomes were noted with the use of corticosteroid injections and electrical stimulation and conflicting results were seen regarding the use of BTx-A. Overall, these targeted therapies provide benefit in the treatment of HSP in individuals who are more than 6 months post 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".