Caution when using publicly available datasets
Notice bibliographique
Résumé
We read, with interest, the paper by Maenosono et al. entitled, “Recipient Sex and Estradiol Levels Affect Transplant Outcomes in an Age-Specific Fashion.”1 We commend the authors on their efforts to deepen understanding of the mechanisms underlying the age-dependent sex differences in graft survival that have been observed in humans. We were surprised to see the analysis of differences in graft survival by recipient sex among patients recorded in the Scientific Registry of Transplant Recipients (SRTR) presented as a novel finding. Our group previously identified important donor sex-dependent and age-dependent differences in graft failure risk between male and female kidney transplant recipients using the SRTR data; this study, entitled “Association of Sex with Risk of Kidney Graft Failure Differs by Age,” was published in 2017.2 Not surprisingly, the findings of Maenosono et al., showing higher graft failure rates in young women than men but lower graft failure rates in older women than men, confirm our published findings in a very similar SRTR cohort. This replication should remind researchers and reviewers alike of the importance of a thorough review of existing literature, especially when analyzing publicly available datasets. The accompanying animal studies aimed at determining the contribution of estradiol to the observed sex differences in graft failure risk are novel and begin to answer some of the questions regarding mechanisms underlying observed sex differences in graft survival. However, additional questions remain. For example, the authors emphasize the differences in graft survival between young and old female mice, but do not discuss differences in graft survival between young and old male mice. We and others have demonstrated higher graft failure rates in late adolescent and early young adult than older kidney, liver, and heart transplant recipients.3-5 The association between age and graft failure rate does not differ by recipient sex.5 The experiments of Maenosono et al. suggest that higher estradiol levels during peak reproductive years may be at least partly responsible for the higher graft failure rates observed in younger than older female recipients. This is an important observation. Given that the higher graft failure rates in young (vs. older) human transplant recipients are generally attributed to poorer adherence to immunosuppressive medications, important observations such as these provide evidence that non-behavioral factors may also contribute. Similar animal studies assessing differences in graft survival and in immune profiles by age in males are encouraged. More studies examining sex differences in graft outcomes and identifying mechanisms for these differences are needed. A comprehensive review of existing literature is encouraged to effectively target appropriate avenues of inquiry. 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 | 0,091 | 0,400 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,011 | 0,011 |
| Science ouverte | 0,006 | 0,007 |
| Intégrité de la recherche | 0,026 | 0,033 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,017 |
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 ».