Co-enrollment of critically ill patients into multiple studies: patterns, predictors and consequences
Bibliographic record
Abstract
INTRODUCTION: Research on co-enrollment practices and their impact are limited in the ICU setting. The objectives of this study were: 1) to describe patterns and predictors of co-enrollment of patients in a thromboprophylaxis trial, and 2) to examine the consequences of co-enrollment on clinical and trial outcomes. METHODS: In an observational analysis of an international thromboprophylaxis trial in 67 ICUs, we examined the co-enrollment of critically ill medical-surgical patients into more than one study, and examined the clinical and trial outcomes among co-enrolled and non-co-enrolled patients. RESULTS: Among 3,746 patients enrolled in PROTECT (Prophylaxis for ThromboEmbolism in Critical Care Trial), 713 (19.0%) were co-enrolled in at least one other study (53.6% in a randomized trial, 37.0% in an observational study and 9.4% in both). Six factors independently associated with co-enrollment (all P < 0.001) were illness severity (odds ratio (OR) 1.35, 95% confidence interval (CI) 1.19 to 1.53 for each 10-point Acute Physiology and Chronic Health Evaluation (APACHE) II score increase), substitute decision-makers providing consent, rather than patients (OR 3.31, 2.03 to 5.41), experience of persons inviting consent (OR 2.67, 1.74 to 4.11 for persons with > 10 years' experience compared to persons with none), center size (all ORs > 10 for ICUs with > 15 beds), affiliation with trials groups (OR 5.59, 3.49 to 8.95), and main trial rather than pilot phase (all ORs > 8 for recruitment year beyond the pilot). Co-enrollment did not influence clinical or trial outcomes or risk of adverse events. CONCLUSIONS: Co-enrollment was strongly associated with features of the patients, research personnel, setting and study. Co-enrollment had no impact on trial results, and appeared safe, acceptable and feasible. Transparent reporting, scholarly discourse, ethical analysis and further research are needed on the complex topic of co-enrollment during critical illness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".