213.2: Referral rates and hospitalization characteristics of referred and non-referred patients meeting GIVE clinical triggers prior to enactment of mandatory referral legislation
Bibliographic record
Abstract
Introduction: Adequate legislation is a key component of high performing deceased donation systems. However, success is also reliant on healthcare provider identification and timely referral of patients meeting clinical triggers. Awareness, attitudes and knowledge may be associated with identification and referral. Non-referral has previously been attributed to patient characteristics such as older age, death from non-neurologic causes, and presence of chronic organ disease; other factors may include hemodynamic instability, palliation without planned WLST, and next-of-kin decline prior to intended referral. In 2021 in NS enacted mandatory referral legislation for patients meeting clinical triggers and intentionally broadened referral criteria to shift responsibility of identifying medically suitable potential donors from bedside clinicians to organ donation specialists. This study aimed to compare the hospital characteristics of patients referred and not referred to the organ donation program prior to these system changes. Method: Retrospective audit of Legacy of Life database for all patient deaths in Nova Scotia Health hospitals between 2017 and 2020 (4 years). Patients meeting clinical triggers for referral (primarily ventilated within 12 hours of death) were included, excluding patients who died during unsuccessful cardiopulmonary resuscitation. We examined annual referral rates, reasons for non-referral, and compared hospitalization characteristics of referred and non-referred patients. Results: Of 1699 patient deaths meeting clinical triggers for referral, 186 (10.9%) were referred. Referral rates increased from 4.8% in 2017 to 16.8% in 2020. The most common reason listed for non-referral (n=1513) was imminent move to comfort care requested by next-of-kin (n=1295; 85.6%). Although there were a similar number of deaths in academic hospitals (n=972; 57.2%) and non-academic hospitals (n=727; 42.8%), a higher proportion of deaths in academic hospitals (n=154; 15.8%) were referred than in non-academic hospitals (n=32; 4.4%). Higher rates of referral were observed in units with a greater number of deaths – Intensive Care Units (176 of 1399; 12.6%), Intermediate Care Units (5 of 135; 3.7%), Emergency Department (5 of 165; 3.0%). Conclusion: We documented a high rate of missed referrals of patients meeting clinical triggers for referral to the organ donation program in Nova Scotia in the 4 years prior to enactment of mandatory referral legislation and intentionally broadening referral criteria to shift responsibility of identifying medically suitable potential donors from bedside clinicians to organ donation specialists. Targeted educational initiatives with particular focus on non-academic centres and areas outside the Intensive Care Unit may be beneficial. Further exploration of reasons for non-referral is required.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".