The effectiveness of structured patient education for the management of musculoskeletal disorders and injuries of the extremities: a systematic review by the Ontario Protocol for Traffic Injury Management (OPTIMa) Collaboration.
Bibliographic record
Abstract
PURPOSE: To determine the effectiveness of structured patient education for the management of musculoskeletal disorders and injuries of the extremities. METHODS: We searched MEDLINE, EMBASE, CINAHL, PsycINFO, and the Cochrane Central Register of Controlled Trials from January 1, 1990 to March 14, 2015. Paired reviewers independently screened titles and abstracts for eligibility. The internal validity of studies was assessed using the Scottish Intercollegiate Guidelines Network (SIGN) criteria. Results from studies with a low risk of bias were synthesized using the best-evidence synthesis methodology. RESULTS: We identified two randomized trials with a low risk of bias. Our review suggests that: 1) multimodal care and corticosteroid injections lead to faster pain relief and improvement than reassurance and advice in the short-term and similar outcomes in the long-term for patients with persistent lateral epicondylitis; and 2) providing health education material alone may be less effective than multimodal care for the management of persistent patellofemoral pain syndrome. CONCLUSION: Our systematic search of the literature demonstrates that little is known about the effectiveness of structured patient education for the management of musculoskeletal disorders and injuries of the extremities. Two studies suggest that when used alone, structured patient education may be less effective than other interventions used to manage persistent lateral epicondylitis and persistent patellofemoral syndrome.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".