Statistical analysis plan for a cluster-randomised trial assessing the effectiveness of implementation of a bedside evidence-based checklist for clinical management of brain-dead potential organ donors in intensive care units: DONORS (Donation Network to Optimise Organ Recovery Study)
Bibliographic record
Abstract
BACKGROUND: The quality of clinical care of brain-dead potential organ donors may help reduce donor losses caused by irreversible or unreversed cardiac arrest and increase the number of organs donated. We sought to determine whether an evidence-based, goal-directed checklist for donor management in intensive care units (ICUs) can reduce donor losses to cardiac arrest. METHODS/DESIGN: The DONORS study is a multicentre, cluster-randomised controlled trial with a 1:1 allocation ratio designed to compare an intervention group (goal-directed checklist for brain-dead potential organ donor management) with a control group (standard ICU care). The primary outcome is loss of potential donors due to cardiac arrest. Secondary outcomes are the number of actual organ donors and the number of solid organs recovered per actual donor. Exploratory outcomes include the achievement of relevant clinical goals during the management of brain-dead potential organ donors. The present statistical analysis plan (SAP) describes all primary statistical procedures that will be used to evaluate the results and perform exploratory and sensitivity analyses of the trial. DISCUSSION: The SAP of the DONORS study aims to describe its analytic procedures, enhancing the transparency of the study. At the moment of SAP subsmission, 63 institutions have been randomised and were enrolling study participants. Thus, the analyses reported herein have been defined before the end of the study recruitment and database locking. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03179020. Registered on 7 June 2017.
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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 | 0.122 | 0.216 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.120 | 0.010 |
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".