Enrolment patterns in a randomized controlled trial of probiotics in critically ill patients: a retrospective analysis of the PROSPECT trial
Bibliographic record
Abstract
BACKGROUND: Understanding site-related factors that influence enrolment within multicenter randomized controlled trials (RCT) may help reduce trial delays and cost over-runs and prevent early trial discontinuation. In this analysis of PROSPECT (Probiotics: Prevention of Severe Pneumonia and Endotracheal Colonization Trial), we describe patient enrolment patterns and examine factors influencing site-based monthly enrolment. DESIGN: Retrospective analysis of a multicenter RCT. METHODS: The PROSPECT multicenter RCT enrolled patients in the main trial from July 2015 to March 2019. We documented site characteristics and trial metrics including data from the methods center tracking documents, site-level data at trial initiation, screening logs submitted by research coordinators, and prospectively collected case report forms. In this retrospective analysis of trial data, we analyzed enrolment patterns across sites using negative binomial regression to explore the association between monthly enrolment rate accounting for number of ICU beds, site characteristics, and trial metrics. RESULTS: Overall, 41 sites enrolling 2365 patients in the PROSPECT main trial were analyzed. After accounting for number of beds in each ICU, site launch early in the trial was associated with higher monthly enrolment rates, but time to first enrolment and research coordinator experience was not. We observed considerable variability in the number of active screening months and enrolment rates across sites. CONCLUSION: These findings highlight the complexity of recruitment dynamics in critical care RCTs and emphasize the need for tailored approaches to trial planning and execution. TRIAL REGISTRATION: PROSPECT (Probiotics: Prevention of Severe Pneumonia and Endotracheal Colonization Trial): NCT02462590 (registered June 2, 2015).
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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 | 0.098 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".