Evidence mapPaperPMID 38515571Full record

ArticleFrontiers in oncology2024

Trends in survival of ovarian clear cell carcinoma patients from 2000 to 2015.

Bing-Qian Tian, Shu-Wen Wang, Jing-Ying Xu, San-Gang Wu, Juan Zhou

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Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Bing-Qian Tian *School of Medicine, Xiamen University, Xiamen, China.
Shu-Wen Wang *Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Jing-Ying XuDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
San-Gang WuDepartment of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Juan ZhouDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To analyze changes in survival outcomes in patients with ovarian clear cell carcinoma (OCCC) treated consecutively over a 16-year period using a population-based cohort. Methods: We conducted a retrospective analysis of OCCC from 2000 to 2015 using data from the Surveillance, Epidemiology, and End Results (SEER) program. The ovarian cancer-specific survival (OCSS) and overall survival (OS) were analyzed according to the year of diagnosis. Joinpoint Regression Program, Kaplan-Meier analysis, and multivariate Cox regression analyses were used for statistical analysis. Results: We included 4257 patients in the analysis. The analysis of annual percentage change in OCSS (P=0.014) and OS (P=0.006) showed that patients diagnosed in later years had significantly better outcomes compared to those diagnosed in early years. The results of the multivariate Cox regression analyses showed that the year of diagnosis was the independent prognostic factor associated with OCSS (P=0.004) and had a borderline effect on OS (P=0.060). Regarding the SEER staging, the OCSS (P=0.017) and OS (P=0.004) of patients with distant stage showed a significant trend toward increased, while no significant trends were found in the survival of patients with localized or regional stage diseases. Similar trends were found in those aged <65 years or those treated with surgery and chemotherapy. However, no statistically significant changes in the survival rate were found in those aged ≥65 years or those receiving surgery alone regardless of SEER stage during the study period. Conclusions: Our study observed a significant increase in the survival outcomes in OCCC from 2000 to 2015, and patients aged <65 years and those with distant stage experienced a greater improvement in survival.

Indexed as

annual percentage changeclear cell carcinomaovarian cancerSEERsurvival trend

Identifiers

PMID38515571
PMCPMC10956514

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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 Socratic graph.