Criteria to Identify a Potential Deceased Organ Donor: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To systematically review the global published literature defining a potential deceased organ donor and identifying clinical triggers for deceased organ donation identification and referral. DATA SOURCES: Medline and Embase databases from January 2006 to September 2017. STUDY SELECTION: All published studies containing a definition of a potential deceased organ donor and/or clinical triggers for referring a potential deceased organ donor were eligible for inclusion. Dual, independent screening was conducted of 3,857 citations. DATA EXTRACTION: Data extraction was completed by one team member and verified by a second team member. Thematic content analysis was used to identify clinical criteria for potential deceased organ donation identification from the published definitions and clinical triggers. DATA SYNTHESIS: One hundred twenty-four articles were included in the review. Criteria fell into four categories: Neurological, Medical Decision, Cardiorespiratory, and Administrative. Distinct and globally consistent sets of clinical criteria by type of deceased organ donation (neurologic death determination, controlled donation after circulatory determination of death, and uncontrolled donation after circulatory determination of death) are reported. CONCLUSIONS: Use of the clinical criteria sets reported will reduce ambiguity associated with the deceased organ donor identification and the subsequent referral process, potentially reducing the number of missed donors and saving lives globally through increased transplantation.
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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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".