Characteristics and Outcomes of Eligible Nonenrolled Patients in a Mechanical Ventilation Trial of Acute Respiratory Distress Syndrome
Bibliographic record
Abstract
RATIONALE: Patients eligible for randomized controlled trials may not be enrolled for various reasons. Nonenrollment may affect study generalizability and lengthen the time required for trial completion. OBJECTIVES: To describe characteristics and outcomes of eligible nonenrolled (ENE) patients in a multicenter trial of mechanical ventilation strategies. METHODS: Within the OSCILLATE trial of high-frequency oscillation (HFO) versus conventional ventilation (CV) in adults with adult respiratory distress syndrome, and with approval from research ethics boards, we collected a minimal dataset on patients who satisfied eligibility criteria but were not enrolled. We categorized ENE patients as ENE-HFO and ENE-CV based on receipt of HFO at any time. We used multivariable logistic regression to assess the association between ENE status and mortality. MEASUREMENTS AND MAIN RESULTS: A total of 548 patients were randomized, and 546 were ENE. The most common reasons for ENE were no consent (42%), physician refusal (24%), missed randomization window (15%), and current HFO use (14%). Compared with randomized patients in respective arms of the trial, ENE-HFO patients were younger and had worse lung injury, whereas ENE-CV patients had lower illness severity. ENE status was independently associated with mortality (adjusted odds ratio, 1.39; 95% confidence interval, 1.06-1.84; P = 0.02), with no significant interaction with ventilation treatment group. CONCLUSIONS: Nonenrollment was common, with approximately one ENE patient for every randomized patient. Our study suggests that enrollment in trials of mechanical ventilation may be associated with improved outcomes compared with standard care and highlights the need for prospective tracking and transparent reporting of ENE patients as part of trial management.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".