Center effect on posttransplant survival among currently active United States pediatric heart transplant centers
Bibliographic record
Abstract
To the Editor: Singh and Gauvreau have applied advanced sophisticated statistical techniques to SRTR data to identify that center mortality at 90 days predicts late center outcomes and replicated a known finding of a “U”-shaped survival curve based on center volume.1Singh TP, Gauvreau K. Center effect on posttransplant survival among currently active United States pediatric heart transplant centers [published online ahead of print 2018]. Am J Transplant. https://doi.org/10.1111/ajt.14950.Google Scholar The accompanying editorial by Drs Scheel and Canter mostly focuses on why the curve might be U-shaped.2Scheel J, Canter CE. Center volume and outcomes in pediatric heart transplantation-Bigger is better until it isn’t [published online ahead of print 2018]. Am J Transplant. https://doi.org/10.1111/ajt.15034.Google Scholar However neither the paper or nor the editorial addresses the important fact that it is not survival from a given intervention (in this case transplantation) that is most important to the family and patient but the likelihood of survival and quality of life from the time of diagnosis, or at the very least, listing. We know from the paper by Rana et al. that there are large differences in survival from listing dependent on center size—so large that the hazard ratio is 4.5 (compared to the hazard ratio of 1.39 for survival from transplant in Singh’s paper) and thus dwarfs the posttransplant survival effect.3Rana A Fraser CD Scully BB et al.Inferior outcomes on the waiting list in low-volume pediatric heart transplant centers.Am J Transplant. 2017; 17: 1515-1524Abstract Full Text Full Text PDF PubMed Scopus (29) Google Scholar Even assessing survival from listing is not enough. Acceptance or otherwise onto the transplant list is very variable among centers and obviously will have a large bearing on patient survival. Survival is of course only one measure of a transplant center’s success. The pediatric transplant community continues to invest in studies that focus on mortality where others, the National Pediatric Cardiology-Quality Improvement Collaborative for example, are now focusing on reducing morbidity and improving quality of life.4NPC-QIC. https://npcqic.org. Accessed February 8, 2018.Google Scholar We should follow their lead. Measures do exist, for example the Lansky Scale, to assess functional status posttransplant.5Lansky SB List MA Lansky LL Ritter-Sterr C Miller DR The measurement of performance in childhood cancer patients.Cancer. 1987; 60: 1651-1656Crossref PubMed Scopus (288) Google Scholar Indeed the United Network for Organ Sharing (UNOS) in the United States mandates the collection of functional information—not only for the Lansky Scale but also for the assessment of cognitive and motor development but these are not publically reported. While many of these functional assessments and scores are subjective just because something is easy to measure (eg, survival from transplant) other, less measurable outcomes, are no less important. So until such time as transplant centers’ outcomes take into account quality of life and morbidity and mortality from referral, speculation as to which center would serve a patient and their family better remains just that—speculation. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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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 | 0.010 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".