Identifying common core outcome domains from core outcome sets of musculoskeletal conditions: protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: Core outcome sets (COSs) aim to reduce outcome heterogeneity in clinical practice and research by suggesting a minimum number of agreed-upon outcomes in clinical trials. Most COSs in the musculoskeletal field are developed for specific conditions. We propose that there are likely to be common core domains within existing musculoskeletal COSs that may be used as a starting point in the development of future COSs. We aim to identify common core domains from existing COSs and to facilitate the development of new COSs for musculoskeletal conditions. As a secondary aim, we will assess the development quality of these COSs. METHODS: A systematic review including musculoskeletal COSs. We will search Core Outcome Measures in Effectiveness Trials (COMET) database, MEDLINE, EMBASE, Scopus, Cochrane Methodology Register and International Consortium for Health Outcome Measurement (ICHOM). Studies will be included if related to the development of a COS in adults with musculoskeletal conditions and for any type of intervention. Quality will be assessed using the Core Outcome Set-Standards for Development (COS-STAD) recommendations. Data extracted will include scope of the COS, health condition, interventions and outcome domains. Primary outcomes will be all core domains recommended within each COS. We define a common core outcome domain as one present in at least 67% of all COSs. All findings will be summarized and presented using descriptive statistics. DISCUSSION: This systematic review of COSs will describe the core domains recommended within each musculoskeletal COS. Common domains found may be used in the initial stages of development of future musculoskeletal COSs. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021239141.
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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 | 0.111 | 0.162 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.022 | 0.023 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".