Delayed Referral Results in Missed Opportunities for Organ Donation After Circulatory Death
Bibliographic record
Abstract
OBJECTIVES: Rates of organ donation and transplantation have steadily increased in the United States and Canada over the past decade, largely attributable to a notable increase in donation after circulatory death. However, the number of patients awaiting solid organ transplantation continues to remain much higher than the number of organs transplanted each year. The objective of this study was to determine the potential to increase donation rates further by identifying gaps in the well-established donation after circulatory death process in Ontario. DESIGN: Retrospective cohort study. SETTING: Provincial organ procurement organization. PATIENTS: Patients who died in designated donation hospitals within the province of Ontario, Canada between April 1, 2013, and March 31, 2015. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 1,407 patient deaths following planned withdrawal of life-sustaining therapy, 54.0% (n = 760) were medically suitable for donation after circulatory death. In 438 cases where next of kin was approached, consent rates reached 47.5%. A total of 119 patients became actual organ donors. Only 66.2% (n = 503) of suitable patients were appropriately referred, resulting in 251 missed potential donors whose next of kin could not be approached regarding organ donation because referral occurred after initiation of withdrawal of life-sustaining therapy or not at all. CONCLUSIONS: The number of medically suitable patients who die within 2 hours of planned withdrawal of life-sustaining therapy is nearly six times higher than the number of actual organ donors, with the greatest loss of potential due to delayed referral until at the time of or after planned withdrawal of life-sustaining therapy. Intensive care teams are not meeting their ethical responsibility to recognize impending death and appropriately refer potential organ donors to the local organ procurement organization. In cases where patients had previously registered their consent decision, they were denied a healthcare right.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".