Does shared decision making results in better health related outcomes for individuals with painful musculoskeletal disorders? A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it