The Impact of Ex Situ Heart Perfusion in Pediatric Transplantation: An Analysis of the United Network for Organ Sharing Registry [RETRACTED]
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Ex situ heart perfusion (ESHP) has increased the pool of donors in adults. However, this is not true in pediatrics due to lack of devices. Therefore, we sought to understand organ refusal in pediatrics and estimate donor heart usage with ESHP. Donor hearts offered to pediatrics were identified from the United Network for Organ Sharing (UNOS) Database (2000-2019). A linear regression model was built to predict average travel speed, and the extended maximum permitted distance with ESHP was calculated. This extended distance was compared with the policy for maximum travel distance. There were 33,708 donor offers (n = 10,807 hearts) to pediatric programs (24.1% [n = 2,604] transplanted). Six percent of the offers (n = 1,832) (n = 771 hearts) were turned down due to distance, with 676 of the hearts never transplanted. Based on the modeling and using an ESHP time of 5.5 hours, 84% (n = 570/676) of hearts turned down as distance could be utilized by pediatric programs. This proportion increased to 100% with 10 hours of support time. By addressing prolonged ischemic time due to distance, ESHP has the potential to increase the number of donors utilized in pediatric candidates. While no device exists for pediatrics, this analysis lends support to the importance of developing this technology.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".