Developing Core Outcome (Measurement) Sets for Critical Care Research Using the Modified Delphi Method
Bibliographic record
Abstract
TOPIC IMPORTANCE: High-quality core outcome sets (COSs) and core outcome measurement sets (COMSs) can help to optimize research by allowing the results of clinical trials to be compared and combined in systematic reviews. The number of registered COSs and COMSs for critical care research is increasing, and most are developed using the Delphi method. However, the quality of these tools varies substantially. REVIEW FINDINGS: At least 39 COSs and 10 associated COMSs have been designed for clinical research in critical care and at least 21 ongoing development projects. The Delphi method is the most common method used to foster agreement on the content of a COS or COMS. It is flexible and permits the development process to be tailored to the medical condition and population of interest. However, designing an effective Delphi study requires time and careful deliberation. Clearly defining scope, piloting survey materials, and crafting a consensus process that uses the strengths of each stakeholder group and minimizes loss to follow-up are encouraged. Reporting on COS and COMS development should be sufficiently detailed for readers to understand and critique both the process and the resulting research tool. Established checklists and guidelines are available to assist with both protocol development and peer review of manuscripts reporting on newly generated COSs and COMSs. SUMMARY: Thorough preliminary work, planning, and reporting increase the likelihood that COSs or COMSs related to critical care will reflect the opinions of knowledgeable stakeholders and will improve the usefulness of clinical trial data.
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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 | 0.461 | 0.532 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".