Does shared decision making results in better health related outcomes for individuals with painful musculoskeletal disorders? A systematic review
Bibliographic record
Abstract
Background: Shared Decision-Making (SDM) is a dynamic process by which the health care professional and the patient influence each other in making health-related choices or decisions. SDM is strongly embedded in today’s health care approaches, and is advocated as an ideal model since it renders individuals more control towards the health care they choose to receive, and has been shown to improve patient outcomes.Objectives: The goal of this systematic review was to investigate the added-value of SDM on clinical health-related outcomes in patients with a variety of musculoskeletal conditions.Data sources: PubMed and CINAHL.Study selection: PRISMA guidelines were followed for this review. To be considered for review, the study had to meet all the following criteria: (1) prospective studies that involved treatment decision-making; (2) randomized controlled trial design; (3) involving patients faced with having to make a treatment decision; (4) comparing SDM with a control intervention and (5) including one or more of the following outcome measures: well-being, costs, health-related pain or disability measures, or quality of life.Study appraisal: A priori, we determined to perform methodological quality assessment using the Cochrane Risk of Bias tool for randomized controlled trials.Results: We did not find a single study that looked at the true effect of SDM on patient reported outcomes in a population with musculoskeletal pain.Conclusion: For the management of painful musculoskeletal conditions, in the light of the current evidence (none), we estimate that it would be wise to explore the effectiveness of SDM before forcing its large-scale implementation in rehabilitation.
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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 | 0.021 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".