Evidence map›Paper›PMID 42292192›Full record

ArticleFrontiers in medicine2026

From discarding to leveraging: quality-aware collaborative learning for robust diabetic retinopathy grading.

Yuan Pan, Xiangwen Cai, Pan Xiong, Fang Mei, Zhengfei Wang, Jiaming Hong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medicine, 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

6 authors.

Yuan PanSchool of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
Xiangwen CaiGuangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Pan XiongSchool of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
Fang MeiSchool of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
Zhengfei WangSchool of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.
Jiaming HongSchool of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Real-world diabetic retinopathy (DR) screening faces a paradox: the most diagnostically critical images are often the lowest in quality, because advanced disease itself produces vitreous hemorrhage, proliferative tissue, and media opacities that degrade fundus imagery. We characterize this quality-severity coupling quantitatively (Spearman ρ = 0.420, odds ratio 4.17 for referable DR in Reject vs. Good strata on DDR,

Indexed as

collaborative learningdiabetic retinopathy gradingdynamic expert routingfundus image quality assessmentmulti-scale feature learning

Identifiers

PMID42292192
PMCPMC13259753

What OpenQuestion holds

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Registered trials

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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.