Development and validation of a nomogram for predicting endoscopic healing in crohn’s disease patients receiving infliximab treatment

This TDM- and PLR-based nomogram enables early identification of IFX responders, potentially reducing unnecessary endoscopic evaluations and guiding personalized therapy in CD management.

Authors

Institutions

Publication Details

Journal
BMC Gastroenterology
Published
2025-07-01
DOI
https://doi.org/10.1186/s12876-025-04070-z
Citations
1
Primary Topic
Inflammatory Bowel Disease
Type
article
Field-Weighted Citation Impact
1.82

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and validation of a nomogram for predicting endoscopic healing in crohn’s disease patients receiving infliximab treatment

Ting Feng, Xuanzhi Huang, Minhu Chen, Lixuan Li et al.
1 citations
BMC Gastroenterology
Inflammatory Bowel Disease
1.82
article

Development and validation of a nomogram for predicting endoscopic healing in crohn’s disease patients receiving infliximab treatment

Ting Feng, Xuanzhi Huang, Minhu Chen, Lixuan Li, Yun Qiu, Xiaoman Zu
article en
1 citations

Abstract

This TDM- and PLR-based nomogram enables early identification of IFX responders, potentially reducing unnecessary endoscopic evaluations and guiding personalized therapy in CD management.

BMC GastroenterologyVol. 25(1)
Sun Yat-sen University (CN), The First Affiliated Hospital, Sun Yat-sen University (CN)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 14%
Inflammatory Bowel Disease
1.82
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.