Choosing important health outcomes for comparative effectiveness research: 6th annual update to a systematic review of core outcome sets for research
Bibliographic record
Abstract
BACKGROUND: An annual update to a systematic review of core outcome sets (COS) for research ensures that the COMET database is up-to-date. The aims of this study were to: (i) identify COS that were published or indexed in 2019 and to describe the methodological approaches used in these studies; (ii) investigate whether children have been included as participants in published COS development studies, and which methods have been used to facilitate their participation; iii) update a previous exercise to identify COS relevant to the most burdensome global diseases and injuries. METHODS: MEDLINE and SCOPUS were searched to identify studies published or indexed between (and inclusive of) January 2019 and December 2019. Automated screening methods were used to rank the citations in order of relevance; the top 25% in ranked priority order were screened for eligibility. COS were assessed against each of the Core Outcome Set-STAndards for Development (COS-STAD). A search of the COMET database was undertaken to identify COS relevant to the 25 leading causes of disease burden. RESULTS: Thirty-three studies, describing the development of 37 COS, were included in this update. These studies have been added to the COMET database, which now contains 370 published (1981-2019) COS studies for clinical research. Six (18%) of the 33 studies in this update were deemed to have met all of the minimum standards for COS development (range = 4 to 12 criteria, median = 9 criteria). Of the 370 COS studies published to date, 82 COS have been developed for paediatric health conditions and children would have been eligible to participate in 68/82 of these studies. Eleven of these 68 (16%) COS studies have included children as participants within the development process, most commonly through participation in Delphi surveys. Relevant COS were identified for 22/25 leading causes of global disease burden. CONCLUSION: There has been a demonstrated increase in COS developed for both research and routine practice, and consistently high inclusion of patient participants. COS developed for paediatric conditions need to further incorporate the perspectives of children, alongside parents and other adults, and adopt research methods fit for this purpose. COS developers should consider the gaps identified in this update as priorities for COS development.
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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 | 0.419 | 0.616 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.021 |
| Bibliometrics | 0.059 | 0.036 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".