DONORS (Donation Network to Optimise Organ Recovery Study): Study protocol to evaluate the implementation of an evidence-based checklist for brain-dead potential organ donor management in intensive care units, a cluster randomised trial
Bibliographic record
Abstract
INTRODUCTION: There is an increasing demand for multi-organ donors for organ transplantation programmes. This study protocol describes the Donation Network to Optimise Organ Recovery Study, a planned cluster randomised controlled trial that aims to evaluate the effectiveness of the implementation of an evidence-based, goal-directed checklist for brain-dead potential organ donor management in intensive care units (ICUs) in reducing the loss of potential donors due to cardiac arrest. METHODS AND ANALYSIS: The study will include ICUs of at least 60 Brazilian sites with an average of ≥10 annual notifications of valid potential organ donors. Hospitals will be randomly assigned (with a 1:1 allocation ratio) to the intervention group, which will involve the implementation of an evidence-based, goal-directed checklist for potential organ donor maintenance, or the control group, which will maintain the usual care practices of the ICU. Team members from all participating ICUs will receive training on how to conduct family interviews for organ donation. The primary outcome will be loss of potential donors due to cardiac arrest. Secondary outcomes will include the number of actual organ donors and the number of organs recovered per actual donor. ETHICS AND DISSEMINATION: The institutional review board (IRB) of the coordinating centre and of each participating site individually approved the study. We requested a waiver of informed consent for the IRB of each site. Study results will be disseminated to the general medical community through publications in peer-reviewed medical journals. TRIAL REGISTRATION NUMBER: NCT03179020; Pre-results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.050 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.082 | 0.014 |
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".