A Core Outcome Set for Adult General ICU Patients
Bibliographic record
Abstract
OBJECTIVES: Randomized clinical trials informing clinical practice (e.g., like large, pragmatic, and late-phase trials) should ideally mostly use harmonized outcomes that are important to patients, family members, clinicians, and researchers. Core outcome sets for specific subsets of ICU patients exist, for example, respiratory failure, delirium, and COVID-19, but not for ICU patients in general. Accordingly, we aimed to develop a core outcome set for adult general ICU patients. DESIGN: We developed a core outcome set in Denmark following the Core Outcome Measures in Effectiveness Trials Handbook. We used a modified Delphi consensus process with multiple methods design, including literature review, survey, semi-structured interviews, and discussions with initially five Danish research panels. The core outcome set was internationally validated and revised based on feedback from research panels in all countries. SETTING: There were five Danish research panels and 17 panels in 13 other countries. Interviews and the three-round Delphi survey was conducted in Denmark, followed by validation of the core outcome set across 14 countries in Europe, Australasia, and India. SUBJECTS: Adult ICU survivors, family members, clinicians, and researchers. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified 329 published outcomes, of which 50 were included in the 264 participant Delphi survey. In semi-structured interviews of 82, no additional outcomes were added. The first Delphi survey round was completed by 249 (94%) participants, and 202 (82%) contributed to the third and final round. The initial core outcome set comprised six outcomes. International validation involved 217 research panel members and resulted in the final core outcome set comprising survival, free of life support, free of delirium, out of hospital, health-related quality of life, and cognitive function. CONCLUSIONS: We developed and internationally validated a core outcome set with six core outcomes to be used in research, specifically clinical trials involving adult general ICU patients.
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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 | 0.111 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".