A coordinated approach to improving pediatric heart transplant waitlist outcomes: A summary of the ACTION November 2019 waitlist outcomes committee meeting
Bibliographic record
Abstract
The number of children needing heart transplantation continues to rise. Although improvements in heart failure therapy, particularly durable mechanical support, have reduced waitlist mortality, the number of children who die while waiting for a suitable donor organ remains unacceptably high. Roughly, 13% of children and 25% of infants on the heart transplant waitlist will not survive to transplantation. With this in mind, the Advanced Cardiac Therapies Improving Outcomes Collaborative Learning Network (ACTION), through its Waitlist Outcomes Committee, convened a 2-day symposium in Ann Arbor, Michigan, from 2-3 November 2019, to better understand the factors that contribute to pediatric heart transplant waitlist mortality and to focus future efforts on improving the organ allocation rates for children needing heart transplantation. Using improvement science methodology, the heart failure-transplant trajectory was broken down into six key steps, after which modes of failure and opportunities for improvement at each step were discussed. As a result, several projects aimed at reducing waitlist mortality were initiated.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".