Reply
Notice bibliographique
Résumé
Potential conflict of interest: Dr. Patel consults for, advises for, and received grants from Gilead. Dr. Hansen: has received grants from Intercept, CymaBay, and is a consultant for Intercept, CymaBay, Albireo, Mirum and ChemomAb. Dr. Janssen consults for and received grants from AbbVie, Arbutus, Bristol‐Myers Squibb, Gilead, Janssen, Medimmune, Merck, Roche, and Vir. He consults for Arena, Benitec, Enjoy, GlaxoSmithKline, Myr, Springbank, and Viro. We thank Dr. Huang and Dr. Li for their interest in our study. We agree that antiviral treatment affects outcomes in chronic hepatitis B (CHB) patients. In our study, antiviral therapy use was only significant in univariable analysis, but did not reach significance in the multivariable model for clinical events (P = 0.134). Addition of antiviral treatment to the selected model did not reach significance (P = 0.127) and had no effect on the hazard ratios of other variables. At both tertiary centers, patients were treated with antiviral regimens to ensure effective viral suppression. We were not able to further stratify our analysis based on the types of antiviral drugs to assess their effects on clinical outcomes independently of disease severity or viral suppression. Recent observational studies have attempted to address this with conflicting results, and currently there is no consensus regarding such differences between available therapies.1 We agree with Dr. Li that hepatic steatosis should be separated from non‐alcoholic steatohepatitis (NASH). We have shown that in contrast to NASH, hepatic steatosis was not associated with worse outcomes in our adjusted analysis. Regarding controlling for advanced fibrosis (AF) risk factors, we have clearly shown that NASH was independently and significantly associated with worse outcomes when adjusted for age, sex, hepatitis B e antigen status, presence of AF, and diabetes mellitus. We agree with Dr. Li’s observation that AF alone is a major determinant of adverse outcomes. However, patients with NASH were more likely to have AF, and in cases of AF, superimposed NASH was a detrimental factor associated with worse outcomes, as compared to patients with AF having no‐NASH (adjusted hazard ratio [95% confidence interval], 2.1 [1.2‐3.8]; P = 0.01). We were not able to obtain detailed information on concomitant drugs over the prolonged study timeline. However, even if statin and aspirin were independently associated with improved liver outcomes in CHB, their increased use in patients with metabolic syndrome and/or NAFLD would have been expected to weaken the adverse effect of NASH on outcomes in CHB. We agree that there may be confounding variables related to the tertiary center cohort and study duration. However, we would not be able to include NASH as an important outcome variable without a liver biopsy as the reference standard. Certainly, before the advent of noninvasive methods to assess fibrosis, biopsies were done with less‐stringent selection criteria. Although patients who had been selected for biopsy may have been at higher clinical risk of hepatic injury, our baseline characteristics indicated that 51% of patients had minimal F0‐F1 fibrosis. Furthermore, a long enrollment period and follow‐up were imperative to capture clinical outcomes and assess the long‐term effects of concurrent CHB and NASH.
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 | 0,003 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,011 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,102 | 0,057 |
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 ».