THE EFFICACY OF TAPING FOR ROTATOR CUFF TENDINOPATHY: A SYSTEMATIC REVIEW AND META-ANALYSIS.
Bibliographic record
Abstract
BACKGROUND: Rotator cuff (RC) tendinopathy is a highly prevalent musculoskeletal disorder. Non-elastic taping (NET) and kinesiology taping (KT) are common interventions used by physiotherapists. However, evidence regarding their efficacy is inconclusive. OBJECTIVE: To examine the current evidence on the clinical efficacy of taping, either NET or KT, for the treatment of individuals with RC tendinopathy. STUDY DESIGN: Systematic review and meta-analysis. METHODS: A literature search was conducted in four bibliographical databases to identify randomized controlled trials (RCT) that compared NET or KT to any other intervention or placebo for treatment of RC tendinopathy. Internal validity of RCTs was assessed with the Cochrane Risk of Bias tool. A qualitative or quantitative synthesis of evidence was performed. RESULTS: Ten trials were included in the present review on overall pain reduction or improvement in function. Most RCTs had a high risk of bias. There is inconclusive evidence for NET, either used alone or in conjunction with another intervention. Based on pooled results of two studies (n=72), KT used alone resulted in significant gain in pain free flexion (MD: 8.7 ° 95%CI 8.0 ° to 9.5 °) and in pain free abduction (MD: 10.3 ° 95%CI 9.1 ° to 11.4 °). Based on qualitative analyses, there is inconclusive evidence on the efficacy of KT when used alone or in conjunction with other interventions on overall pain reduction or improvement in function. CONCLUSION: Although KT significantly improved pain free range of motion, there is insufficient evidence to formally conclude on the efficacy of KT or NET used alone or in conjunction with other interventions in patients with RC tendinopathy. LEVEL OF EVIDENCE: Therapy, level 1a.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".