Evidence map›Paper›PMID 42040622›Full record

ArticleFrontiers in medicine2026

Psychosocial determinants of anti-VEGF treatment adherence in AMD patients: optimization of one-stop intravitreal injection service model.

Xi Zhang, Bingjie Cui, Yingyue Liu, Xiangning Ji, Xiaoyu Tian, Siqing Hou, Lidong Yang, Junshu Yang

Abstract read
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Xi ZhangOphthalmology Medical Center, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Bingjie CuiOphthalmology Medical Center, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Yingyue LiuCangzhou Eye Hospital, Cangzhou, Hebei, China.
Xiangning JiOphthalmology Medical Center, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Xiaoyu TianOphthalmology Medical Center, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Siqing HouOphthalmology Medical Center, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Lidong YangOphthalmology Medical Center, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Junshu YangHebei Medical University, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate modifiable psychosocial determinants of anti-VEGF treatment adherence in patients with neovascular age-related macular degeneration (nAMD) and evaluate the optimization effects of a one-stop intravitreal injection service model. Methods: This historical control mixed-methods study included patients receiving anti-VEGF treatment at Cangzhou Regional Ophthalmology Center from August 2022 to October 2024. Patients were divided into three groups based on service models: traditional multiple-visit group (historical control, Results: Compared to the historical control group, the one-stop standard and AI-enhanced groups showed significantly reduced clinic-to-injection time (23.87 vs. 6.47 vs. 6.01 h, Conclusion: The one-stop intravitreal injection service model significantly improved treatment adherence in nAMD patients, with AI-enhanced intervention further optimizing outcomes. Baseline anxiety and depression levels, along with geographic distance, are important modifiable determinants of treatment adherence. Personalized service models integrating psychosocial interventions provide new insights for precision management of chronic eye diseases.

Indexed as

anti-VEGF treatmentartificial intelligenceneovascular age-related macular degenerationone-stop servicepsychosocial factorstreatment adherence

Identifiers

PMID42040622
PMCPMC13106066

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.