Variability in donor selection among pediatric heart transplant providers: Results from an international survey
Bibliographic record
Abstract
There is considerable variability in donor acceptance practices among adult heart transplant providers; however, pediatric data are lacking. The aim of this study was to assess donor acceptance practices among pediatric heart transplant professionals. The authors generated a survey to investigate clinicians' donor acceptance practices. This survey was distributed to all members of the ISHLT Pediatric Council in April 2018. A total of 130 providers responded from 17 different countries. There was a wide range of acceptable criteria for potential donors. These included optimal donor-to-recipient weight ratio (lower limit: 50%-150%, upper limit: 120%-350%), maximum donor age (25-75 years), and minimum acceptable left ventricular EF (30%-60%). Non-US centers demonstrated less restrictive donor selection criteria and were willing to accept older donors (50 vs 35 years, P < 0.001), greater size discrepancy (upper limit weight ratio 250% vs 200%, P = 0.009), and donors with a lower EF (45% vs 50%, P < 0.001). Recipient factors were most influential in the decision to accept marginal donors including recipients requiring ECMO support, ventilator support, and highly sensitized patients with a negative XM. However, programmatic factors impacted the decision to decline marginal donors including recent programmatic mortalities and concerns for programmatic restrictions from regulatory bodies. There is significant variation in donor acceptance practices among pediatric heart transplant professionals. Standardization of donor acceptance practices through the development of a consensus statement may help to improve donor utilization and reduce waitlist mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".