Choosing important health outcomes for comparative effectiveness research: 4th annual update to a systematic review of core outcome sets for research
Bibliographic record
Abstract
BACKGROUND: The Core Outcome Measures in Effectiveness Trials (COMET) database is a publically available, searchable repository of published and ongoing core outcome set (COS) studies. An annual systematic review update is carried out to maintain the currency of database content. METHODS: The methods used in the fourth update of the systematic review followed the same approach used in the original review and previous updates. Studies were eligible for inclusion if they reported the development of a COS, regardless of any restrictions by age, health condition or setting. Searches were carried out in March 2018 to identify studies that had been published or indexed between January 2017 and the end of December 2017. RESULTS: Forty-eight new studies, describing the development of 56 COS, were included. There has been an increase in the number of studies clearly specifying the scope of the COS in terms of the population (n = 43, 90%) and intervention (n = 48, 100%) characteristics. Public participation has continued to rise with over half (n = 27, 56%) of studies in the current review including input from members of the public. The rate of inclusion of all stakeholder groups has increased, in particular participation from non-clinical research experts has risen from 32% (mean average in previous reviews) to 62% (n = 29). Input from participants located in Australasia (n = 17; 41%), Asia (n = 18; 44%), South America (n = 13; 32%) and Africa (n = 7; 17%) have all increased since the previous reviews. CONCLUSION: This update included a pronounced increase in the number of new COS identified compared to the previous three updates. There was an improvement in the reporting of the scope, stakeholder participants and methods used. Furthermore, there has been an increase in participation from Australasia, Asia, South America and Africa. These advancements are reflective of the efforts made in recent years to raise awareness about the need for COS development and uptake, as well as developments in COS methodology.
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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 | 0.416 | 0.620 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.047 | 0.029 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".