A Core Outcome Set for Multimorbidity Research (COSmm)
Bibliographic record
Abstract
PURPOSE: We aimed to develop a consensus-based set of core outcomes specifically for studies in multimorbidity. METHODS: We undertook a consensus study following the COS-STAR (Core Outcome Set-STAndards for Reporting) guidelines for the design and reporting of core outcome sets. A Delphi panel of experts completed a web-based survey with 2 rounds. Panelists were presented with a range of outcomes that had been identified in previous workshops and a related systematic review. They indicated their level of agreement on whether each outcome should be included in the core set using a 5-point Likert scale, and outcomes reaching a prespecified consensus level were included. RESULTS: Of 30 individuals invited to be panelists, 26 from 13 countries agreed. All 26 completed both rounds of the survey. The Delphi panel reached consensus on 17 outcomes for inclusion in a core outcome set for multimorbidity (COSmm). The highest-ranked outcomes were health-related quality of life, mental health outcomes, and mortality. Other outcomes were grouped into overarching themes of patient-reported impacts and behaviors (treatment burden, self-rated health, self-management behavior, self-efficacy, adherence); physical activity and function (activities of daily living, physical function, physical activity); consultation related (communication, shared decision making, prioritization); and health systems (health care use, costs, quality of health care). CONCLUSIONS: This consensus study involved a wide range of international experts who identified a large number of outcomes for multimorbidity intervention studies. Our results suggest that quality of life, mental health outcomes, and mortality should be regarded as essential core outcomes. Researchers should, however, also consider the full range of outcomes when designing studies to capture important domains in multimorbidity depending on individual study aims and interventions.
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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 | 0.312 | 0.366 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".