Reply
Notice bibliographique
Résumé
Potential conflict of interest: Nothing to report. Author names in bold designate shared co‐first authorship. Thank you for the opportunity to reply to the letter by our colleagues, in which an important point is addressed regarding the interpretation of liver stiffness measurements (LSMs), performed by transient elastography, in population‐based studies. The letter is in reference to our article published in Hepatology in January 2016, in which we described our observations among 3,041 presumed healthy individuals who had undergone hepatic ultrasonography and transient elastography measurements.1 In this population, we found a median LSM of 4.7 kPa (interquartile range 3.8‐5.8), and in 5.6% LSM was ≥8.0 kPa. At this 8.0 kPa cutoff, in most studies as well as in clinical practice, liver fibrosis is diagnosed or, at least, suspected. Thiele et al. rightfully state that liver stiffness does not automatically equate with liver fibrosis, especially in populations with low prevalence of chronic liver disease. We fully agree. However, the true prevalence of liver fibrosis in the general population is unknown. This is because liver biopsy, which is the gold standard of liver fibrosis assessment, is not appropriate for use in the general population, considering its invasive nature. This leaves noninvasive tests, such as transient elastography, as the only usable screening options, albeit with limitations. The real question is how healthy this presumed‐healthy population truly is, given the fact that 24% had abnormal liver enzymes (in which 11.3% had LSM ≥8.0 kPa versus 3.7% in those with normal liver enzymes) and 32.8% had ultrasonographic evidence of nonalcoholic fatty liver disease (NAFLD) (in which 8.4% had LSM ≥8.0 kPa versus 3.5% in those without NAFLD). Furthermore, in the only other population‐based study in Caucasians, by Roulot et al.,2 measuring liver stiffness in 1,190 adults attending a medical checkup, 7.5% were found to have LSM ≥8.0 kPa. It is noteworthy that in all cases an underlying liver disease was found, most of them NAFLD. Given the current NAFLD epidemic, maybe the general population is no longer a low‐prevalence population for chronic liver diseases. We therefore agree with Thiele et al. that early detection of chronic liver disease in the general population is of utmost importance and deserves appropriate attention. In fact, this has become the goal of a recently established research network of European centers for investigation of early detection of chronic liver diseases in the general population. This effort is led by Dr. Gines from the Barcelona group and includes several European investigators, including the group by Thiele et al. and the undersigned.3 Hopefully, in the near future, we will learn from this effort and develop better tools for screening. Until then, the existing noninvasive tools, including transient elastography and serological markers, are likely going to be the only screening tools at our disposal in the general population. Interpretation of these findings should always be balanced against the background prevalence, diagnostic performance, and possible confounders.
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 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| 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 | 0,012 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,105 | 0,067 |
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 ».