Research Consent Models Used in Prospective Studies of Neurologically Deceased Organ Donors: A Systematic Review
Bibliographic record
Abstract
Research to inform the care of neurologically deceased organ donors is complicated by a lack of standards for research consent. In this systematic review, we aim to describe current practices of soliciting consent for participation in prospective studies of neurologically deceased donors, including the frequency and justification for these various models of consent. Among the 74 studies included, 14 did not report on any regulatory review, and 13 did not report on the study consent procedures. Of the remaining 47 studies, 24 utilized a waiver of research consent. The most common justification for a waiver of research consent related to the fact that neurologically deceased donors are not considered human subjects. In conclusion, among studies of neurologically deceased donors, research consent models vary and are inconsistently reported. Consensus and standardization in the application of research consent models will help to advance this emerging field of research.
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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 | 0.146 | 0.466 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".