Evidence map›Paper›PMID 41756783›Full record

ArticleFrontiers in cellular and infection microbiology2026

Interpretable machine learning analysis for relationships between

Chenlu Li, Qingshi Lin, Liaoliao Yang

Abstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2026. 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

3 authors.

Chenlu LiDepartment of Gastroenterology, Affiliated Yueqing Hospital, Wenzhou Medical University, Wenzhou, China.
Qingshi LinInterventional therapy Department, Affiliated Yueqing Hospital, Wenzhou Medical University, Wenzhou, China.
Liaoliao YangDepartment of Gastroenterology, Affiliated Yueqing Hospital, Wenzhou Medical University, Wenzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Helicobacter pylori ( Objectives: This study aimed to evaluate assess the predictive contribution of Methods: A retrospective cohort of 5,862 individuals undergoing routine health check-ups was analyzed. Demographic data, laboratory indices, and lower-extremity vascular ultrasound findings were collected to determine PA status and Results: Conclusions:

Indexed as

AtherosclerosisHelicobacter InfectionsHelicobacter pyloriMachine LearningPeripheral Arterial DiseaseAgedFemaleHumansMalePredictive Learning ModelsRetrospective StudiesRisk FactorsHelicobacter pylorimachine learningmediation analysisperipheral atherosclerosisSHapley additive exPlanations analysis

Identifiers

PMID41756783
PMCPMC12932622

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.