Coenrollment of Critically Ill Patients in PROSPECT: A Protocol and Statistical Analysis Plan
Bibliographic record
Abstract
Abstract Introduction The enrollment of a patient into more than one study (i.e., coenrollment) has risks which warrant exploration, particularly with respect to possible effects on trial outcomes. This pre-planned secondary analysis will examine the sensitivity of treatment effects to coenrollment in an international critical care trial (Probiotics: Prevention of Severe Pneumonia and Endotracheal Colonization Trial (PROSPECT). Objective(s) The primary objective is to evaluate the sensitivity of the effect of probiotics on the primary endpoint of VAP to patient coenrollment in at least one other study. The secondary objectives are to describe the characteristics of coenrolled patients and the studies they were coenrolled into; examine any differences in baseline traits; understand differences in center-level characteristics between coenrolling and non-coenrolling centers; identify factors associated with coenrollment; and explore the relationship between coenrollment status and the incidence of adverse events. Methods We developed a protocol and statistical analysis plan (SAP) for this secondary analysis involving the conduct of a Cox regression model, including treatment allocation, coenrollment status, and the interaction between the two as independent variables. We also describe our planned statistical analyses for the secondary objectives, involving descriptive statistics, univariable analyses, and multivariable analyses. Ethics and Dissemination The results of this study will be published in a peer-reviewed journal focused on critical care research or trial methodology, and presented at local, national, and international conferences. As a secondary analysis, this study does not require research ethics board approval. All data will be presented in aggregate and without patient identifiers.
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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 | 0.252 | 0.274 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.062 | 0.016 |
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".