ArticleFrontiers in pharmacology2026
Incorporating the metabolic ratio (C-
Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Voriconazole (VRC) is widely used as an antifungal agent, yet its potential to cause drug-induced liver injury (DILI) remains a major clinical concern. Therefore, this study aims to identify the relevant risk factors for VRC-induced DILI, construct a predictive model using LASSO-Logistic regression, and present it as a nomogram. Additionally, a web-based prediction tool was developed to facilitate clinical application, ultimately providing a scientific basis for early risk assessment of DILI in patients receiving VRC therapy. Methods: Clinical data of inpatients who received VRC for the treatment or prophylaxis of fungal infections at the First People's Hospital of Changzhou from March 2022 to June 2025 were collected retrospectively. The patients were divided into a DILI group and a non-DILI group according to the diagnostic criteria for DILI. Univariate analysis was used to screen for variables with intergroup differences, followed by LASSO regression for further feature optimization. The screened variables were incorporated into a multivariate Logistic regression to construct a nomogram prediction model for DILI. The receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were adopted to evaluate the discrimination, calibration and clinical applicability of the model, with the Bootstrap method for internal validation. Additionally, a web-based interactive prediction calculator was developed based on R Shiny. Results: A total of 250 patients were enrolled, among whom 47 developed DILI. Univariate analysis revealed statistically significant differences between the DILI group and the non-DILI group in seven variables, namely renal replacement therapy, concomitant bacterial infection, steady-state trough concentration of VRC (C- Conclusion: C-
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.