Organ donation by patients with and without trauma in a Canadian province: a retrospective cohort analysis
Bibliographic record
Abstract
BACKGROUND: People who experience trauma represent a large pool of potential organ donors. Our objective was to describe organ donation by patients with and without trauma in Nova Scotia. METHODS: We performed a retrospective cohort study of all patients with trauma in the Nova Scotia Trauma Registry who were injured between Apr. 1, 2009, and Mar. 31, 2016, and died in hospital, as well as all potential organ donors captured in the Nova Scotia Legacy of Life Donor Registry over the same period. We compared characteristics of the 2 groups with respect to organ donation and identified reasons for nondonation. RESULTS: Overall, 940 patients were included in the analysis, of whom 689 (73.3%) had experienced trauma. Patients with trauma accounted for 37.2% (48/129) of donors. A total of 274 (39.8%) of the patients with trauma were identified as potential organ donors, and 48 (7.0%) donated organs. Only 108 (39.4%) of the 274 were referred to the Legacy of Life Program. The conversion rate (proportion of potential donors who went on to donate an organ) was 84.2% (48/57) among patients with trauma and 83.5% (81/97) among those without trauma. Donation after circulatory death occurred in 8 patients (17%) with trauma and 13 (16%) of those without trauma. Family refusal (28/60 [47%]) and medical unsuitability (16/60 [27%]) were the most common reasons for nondonation among patients with trauma. INTERPRETATION: In Nova Scotia, 40% of patients with trauma who died in hospital were potential organ donors, yet only 39% of these patients were referred for donation. More work is required to improve organ donation within the trauma population.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".