Organ donation after death in Ontario: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Shortfalls in deceased organ donation lead to shortages of solid organs available for transplantation. We assessed rates of deceased organ donation and compared hospitals that had clinical services for transplant recipients (transplant hospitals) to those that did not (general hospitals). METHODS: We conducted a population-based cohort analysis involving patients who died from traumatic brain injury, subarachnoid hemorrhage, intracerebral hemorrhage or other catastrophic neurologic conditions in Ontario, Canada, between Apr. 1, 1994, and Mar. 31, 2011. We distinguished between acute care hospitals with and without transplant services. The primary outcome was actual organ donation determined through the physician database for organ procurement procedures. RESULTS: Overall, 87,129 patients died from catastrophic neurologic conditions during the study period, of whom 1930 became actual donors. Our primary analysis excluded patients from small hospitals, reducing the total to 79,746 patients, of whom 1898 became actual donors. Patients who died in transplant hospitals had a distribution of demographic characteristics similar to that of patients who died in other large general hospitals. Transplant hospitals had an actual donor rate per 100 deaths that was about 4 times the donor rate at large general hospitals (5.0 v. 1.4, p < 0.001). The relative reduction in donations at general hospitals was accentuated among older patients, persisted among patients who were the most eligible candidates and amounted to about 121 fewer actual donors per year (adjusted odds ratio 0.58, 95% confidence interval 0.36-0.92). Hospital volumes were only weakly correlated with actual organ donation rates. INTERPRETATION: Optimizing organ donation requires greater attention to large general hospitals. These hospitals account for most of the potential donors and missed opportunities for deceased organ donation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".