Evidence map›Paper›PMID 40236655›Full record

ArticleFrontiers in oncology2025

Analysis of screen-detected pulmonary nodules before and after the novel coronavirus epidemic: a multicenter retrospective cohort study.

Zemin He, Keting Liu, Ling Wu, Qiang Wei, Qingwei Shen

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

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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

5 authors.

Zemin HeDepartment of Thoracic Surgery, The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University), Chengdu, Sichuan, China.
Keting LiuDepartment of Neurology, Chengdu Seventh People's Hospital, Chengdu, Sichuan, China.
Ling WuDepartment of Respiratory Medicine, The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University), Chengdu, Sichuan, China.
Qiang WeiDepartment of Thoracic Surgery, The First People's Hospital of Shuangliu District (West China Airport Hospital of Sichuan University), Chengdu, Sichuan, China.
Qingwei ShenDepartment of Thoracic Surgery, Sichuan Baoshi Flower Hospital, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To analyze the screening results for pulmonary nodules before and after the COVID-19 epidemic to understand the influence of the COVID-19 epidemic on the detection rate of pulmonary nodules and the detection rate of high-risk pulmonary nodules. Methods: A total of 18,906 chest CT scans were performed between March and November 2022 and between March and November 2023. Subjects from March to December 2022 were divided into the pre-epidemic group, and subjects from March to December 2023 were divided into the post-epidemic group. The detection rates of lung nodules, high risk nodules, different age groups, and gender groups were analyzed. Results: A total of 11513 lung nodules were detected during screening. A total of 841 high risk nodules were detected, and the detection rate of solid nodules was significantly higher in the postepidemic group than in the pre-epidemic group. The detection rate of fibrotic changes was significantly higher in the postepidemic group than in the pre-epidemic group. The detection rate of pulmonary nodules is significantly higher in those aged > 51 years compared to those aged 50 years and below. Conclusion: In the post-COVID-19 period, there has been a significant increase in the detection of solid pulmonary nodules. This increased detection rate is predominantly observed in medical patients over 51 years of age. However, the COVID-19 epidemic has not resulted in an increased detection rate of high risk nodules.

Indexed as

chest CTCOVID-19 epidemiclung cancer screeninglung nodulesreview

Identifiers

PMID40236655
PMCPMC11996628

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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.