Center effect on posttransplant survival among currently active United States pediatric heart transplant centers
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,080 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,006 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».