Reducing donor acceptance practice variation – Learnings from a discussion forum
Bibliographic record
Abstract
PURPOSE: Although waitlist mortality is unacceptably high, nearly half of donor heart offers are rejected by pediatric heart transplant centers. The Advanced Cardiac Therapy Improving Outcome Network (ACTION) and Pediatric Heart Transplant Society (PHTS) convened a multi-institutional donor decision discussion forum (DDDF) aimed at assessing donor acceptance practices and reducing practice variation. METHODS: A 1-h-long virtual DDDF for providers across North America, the United Kingdom, and Brazil was held monthly. Each session typically included two case presentations posing a real-world donor decision challenge. Attendees were polled before the presenting center's decision was revealed. Group discussion followed, including a review of relevant literature and PHTS data. Metrics of participation, participant agreement with presenting center decisions, and impact on future decision-making were collected and analyzed. RESULTS: Over 2 years, 41 cases were discussed. Approximately 50 clinicians attended each call. Risk factors influencing decision-making included donor quality (10), size discrepancy (8), and COVID-19 (8). Donor characteristics influenced 63% of decisions, recipient factors 35%. Participants agreed with the decision made by the presenting center only 49% of the time. Post-presentation discussion resulted in 25% of participants changing their original decision. Survey conducted reported that 50% respondents changed their donor acceptance practices. CONCLUSION: DDDF identified significant variation in pediatric donor decision-making among centers. DDDF may be an effective format to reduce practice variation, provide education to decision-makers, and ultimately increase donor utilization.
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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 | 0.078 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".