Evidence map›Paper›PMID 41158765›Full record

ArticleFrontiers in microbiology2025

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Xi Sun, Jing Liu, Lili Wu, Xiao Chen, Xiaona Ma, Fei Teng, Ting Zhang, Hui Su, Xin Fan, Jiaxin Li and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in microbiology, 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Xi Sun *Department of Gastroenterology and Hepatology, The Second Medical Center of Chinese PLA Hospital and National Clinical Research Center for Geriatric Diseases, Beijing, China.
Jing Liu *Department of Gastroenterology and Hepatology, The Second Medical Center of Chinese PLA Hospital and National Clinical Research Center for Geriatric Diseases, Beijing, China.
Lili WuDepartment of Gastroenterology and Hepatology, The Second Medical Center of Chinese PLA Hospital and National Clinical Research Center for Geriatric Diseases, Beijing, China.
Xiao ChenDepartment of Gastroenterology and Hepatology, The Second Medical Center of Chinese PLA Hospital and National Clinical Research Center for Geriatric Diseases, Beijing, China.
Xiaona MaDepartment of Gastroenterology and Hepatology, The Second Medical Center of Chinese PLA Hospital and National Clinical Research Center for Geriatric Diseases, Beijing, China.
Fei TengDepartment of Gastroenterology and Hepatology, The Second Medical Center of Chinese PLA Hospital and National Clinical Research Center for Geriatric Diseases, Beijing, China.
Ting ZhangThe Institute of Geriatrics, The Second Medical Center of Chinese PLA Hospital and National Clinical Research Center for Geriatric Diseases, Beijing, China.
Hui SuDepartment of Gastroenterology, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Xin FanDepartment of Gastroenterology, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Jiaxin LiDepartment of Geriatrics, Peking University First Hospital, Beijing, China.
Shiping XuDepartment of Gastroenterology and Hepatology, The Second Medical Center of Chinese PLA Hospital and National Clinical Research Center for Geriatric Diseases, Beijing, China.
Peng JinDepartment of Gastroenterology, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
Hongmei JiaoDepartment of Geriatrics, Peking University First Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Accurate classification of Methods: To overcome these limitations, we developed MC-CLIP, a vision-language foundation model for the fully automated, three-categorical diagnosis of Results: On the internal and external validation cohorts, MC-CLIP achieved overall accuracies of 89.6% (95% CI: 85.5-93.6%) and 86.6% (80.8-90.3%), respectively. The model demonstrated particularly high sensitivity in detecting Discussion: By integrating multimodal image-text data and performing end-to-end analysis, MC-CLIP effectively addresses the fundamental limitations of CNN-based approaches. The model shows strong potential for enhancing the accuracy and reliability of MCCE-based gastric cancer screening programs.

Indexed as

artificial intelligencecapsule endoscopegastric cancerHelicobacter pylorilarge language model (LLM)

Identifiers

PMID41158765
PMCPMC12554734

What OpenQuestion holds

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LicenceCC BY
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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.