Evidence map›Paper›PMID 40777994›Full record

ArticleFrontiers in pharmacology2025

Correction: A pathology-attention multi-instance learning framework for multimodal classification of colorectal lesions.

Fanglei Fu, Xuemei Zhang, Zhaoxuan Wang, Luxi Xie, Mingxi Fu, Jing Peng, Jianfeng Wu, Zhe Wang, Tian Guan, Yonghong He and 3 more

Abstract readPublished Erratum
In one paragraph

Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Fanglei Fu *Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
Xuemei Zhang *Department of Pathology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China.
Zhaoxuan Wang *Department of Statistics and Data Science, Washington University in St. Louis, St. Louis, MO, United States.
Luxi XieDepartment of Pathology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China.
Mingxi FuDepartment of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
Jing PengDepartment of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
Jianfeng WuState Key Laboratory of Cancer Biology, Department of Pathology, Xijing Hospital and School of Basic Medicine, Fourth Military Medical University, Xi'an, China.
Zhe WangState Key Laboratory of Cancer Biology, Department of Pathology, Xijing Hospital and School of Basic Medicine, Fourth Military Medical University, Xi'an, China.
Tian GuanDepartment of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
Yonghong HeDepartment of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
Jin-Shun LinDepartment of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
Lianghui ZhuDepartment of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
Wenbin DaiDepartment of Pathology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

[This corrects the article DOI: 10.3389/fphar.2025.1592950.].

Indexed as

colorectal cancermultimodal learningpathology attentionweakly supervised learningwhole slide image classification

Identifiers

PMID40777994
PMCPMC12330284

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.