Evidence map›Paper›PMID 39664185›Full record

ArticleFrontiers in oncology2024

Impact of the Coronavirus disease pandemic on early breast cancer.

Yong Li, Xiao-Ju Lu, Bo Xu, Wei-Wen Li

Abstract read
In one paragraph

Article in Frontiers in oncology, 2024. 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

4 authors.

Yong LiDepartment of Breast, Jiangmen Central Hospital, Jiangmen, China.
Xiao-Ju LuDepartment of Critical Care Medicine, Jiangmen People's Hospital, Jiangmen People's Hospital, Jiangmen, China.
Bo XuDepartment of Breast, Jiangmen Central Hospital, Jiangmen, China.
Wei-Wen LiDepartment of General Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To assess the impact of the COVID-19 pandemic on the presentation, treatment, and survival of patients with early breast cancer (Stage I-III). Methods: This study utilized data from the Surveillance, Epidemiology, and End Results database from January 1, 2018, to December 31, 2020. Patients diagnosed with primary breast cancer in 2020 were compared to those diagnosed in 2018 or 2019. The primary outcomes were stage distribution and changes in the treatment modalities for early breast cancer. The secondary outcomes were overall survival (OS) and breast cancer-specific survival (BCSS). Results: We analyzed 142,038 patients. There has been a decrease in breast cancer diagnoses in 2020, as well as the smaller number of surgeries. The distribution of breast cancer stages among patients exhibited a notable shift in 2020, with a decrease in the proportion of Stage 0-I and an increase in advanced-stage. Additionally, there was a significant decrease in the proportion of breast-conserving surgery (BCS) performed in 2020. The proportion of patients undergoing radiation decreased, while that of chemotherapy cases increased significantly in 2020. Patients showed a shorter treatment delay in 2020 than in 2018 or 2019 (2018: hazard ratio [HR] = 0.969, 95% confidence interval [CI] = 0.956-0.982, Conclusion: We observed a shift to advanced-stage and a change of treatment modalities of early breast cancer in 2020. The OS of patients with breast cancer was worse during the pandemic than before the pandemic. The findings could provide empirical basis for optimizing cancer prevention and control strategies in future public health emergencies.

Indexed as

breast cancercancer diagnosiscancer treatmentCOVID-19 pandemicoverall survival

Identifiers

PMID39664185
PMCPMC11631749

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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.