Can We Decide Which Outcomes Should Be Measured in Every Clinical Trial? A Scoping Review of the Existing Conceptual Frameworks and Processes to Develop Core Outcome Sets
Bibliographic record
Abstract
OBJECTIVE: The usefulness of randomized control trials to advance clinical care depends upon the outcomes reported, but disagreement on the choice of outcome measures has resulted in inconsistency and the potential for reporting bias. One solution to this problem is the development of a core outcome set: a minimum set of outcome measures deemed critical for clinical decision making. Within rheumatology the Outcome Measures in Rheumatology (OMERACT) initiative has pioneered the development of core outcome sets since 1992. As the number of diseases addressed by OMERACT has increased and its experience in formulating core sets has grown, clarification and update of the conceptual framework and formulation of a more explicit process of area/domain core set development has become necessary. As part of the update process of the OMERACT Filter criteria to version 2, a literature review was undertaken to compare and contrast the OMERACT conceptual framework with others within and outside rheumatology. METHODS: A scoping search was undertaken to examine the extent, range, and nature of conceptual frameworks for core set outcome selection in health. We searched the following resources: Cochrane Library Methods Group Register; Medline; Embase; PsycInfo; Environmental Studies and Policy Collection; and ABI/INFORM Global. We also conducted a targeted Google search. RESULTS: Five conceptual frameworks were identified: the WHO tripartite definition of health; the 5 Ds (discomfort, disability, drug toxicity, dollar cost, and death); the International Classification of Functioning (ICF); PROMIS (Patient-Reported Outcomes Measurement System); and the Outcomes Hierarchy. Of these, only the 5 Ds and ICF frameworks have been systematically applied in core set development. Outside the area of rheumatology, several core sets were identified; these had been developed through a limited range of consensus-based methods with varying degrees of methodological rigor. None applied a framework to ensure content validity of the end product. CONCLUSION: This scoping review reinforced the need for clear methods and standards for core set development. Based on these findings, OMERACT will make its own conceptual framework and working process more explicit. Proposals for how to achieve this were discussed at the OMERACT 11 conference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.436 | 0.724 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.039 | 0.032 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".