How Outcomes Are Defined in Clinical Trials of Mechanically Ventilated Adults and Children
Bibliographic record
Abstract
Systematic reviews have considerable potential to provide evidence-based data to aid clinical decision-making. However, there is growing recognition that trials involving mechanical ventilation lack consistency in the definition and measurement of ventilation outcomes, creating difficulties in combining data for meta-analyses. To address the inconsistency in outcome definitions, international standards for trial registration and clinical trial protocols published recommendations, effectively setting the "gold standard" for reporting trial outcomes. In this Critical Care Perspective, we review the problems resulting from inconsistent outcome definitions and inconsistent reporting of outcomes (outcome sets). We present data highlighting the variability of the most commonly reported ventilation outcome definitions. Ventilation outcomes reported in trials over the last 6 years typically fall into four domains: measures of ventilator dependence; adverse outcomes; mortality; and resource use. We highlight the need, first, for agreement on outcome definitions and, second, for a minimum core outcome set for trials involving mechanical ventilation. A minimum core outcome set would not restrict trialists from measuring additional outcomes, but would overcome problems of variability in outcome selection, measurement, and reporting, thereby enhancing comparisons across trials.
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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 | 0.150 | 0.474 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".