Evidence map›Paper›PMID 37091268›Full record

ArticleFrontiers in surgery2023

A nomogram for predicting postoperative overall survival of patients with lung squamous cell carcinoma: A SEER-based study.

Jin Rao, Yue Yu, Li Zhang, Xuefu Wang, Pei Wang, Zhinong Wang

Abstract read
In one paragraph

Article in Frontiers in surgery, 2023. 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

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

4 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

6 authors.

Jin RaoDepartment of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Yue YuDepartment of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Li ZhangDepartment of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Xuefu WangDepartment of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Pei WangDepartment of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China.
Zhinong WangDepartment of Cardiothoracic Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung squamous cell carcinoma (LSCC) is a common subtype of non-small cell lung cancer. Our study aimed to construct and validate a nomogram for predicting overall survival (OS) for postoperative LSCC patients. Methods: A total of 8,078 patients eligible for recruitment between 2010 and 2015 were selected from the Surveillance, Epidemiology, and End Results database. Study outcomes were 1-, 2- and 3-year OS. Analyses performed included univariate and multivariate Cox regression, receiver operating characteristic (ROC) curve construction, calibration plotting, decision curve analysis (DCA) and Kaplan-Meier survival plotting. Results: Seven variables were selected to establish our predictive nomogram. Areas under the ROC curves were 0.658, 0.651 and 0.647 for the training cohort and 0.673, 0.667 and 0.658 for the validation cohort at 1-, 2- and 3-year time-points, respectively. Calibration curves confirmed satisfactory consistencies between nomogram-predicted and observed survival probabilities, while DCA confirmed significant clinical usefulness of our model. For risk stratification, patients were divided into three risk groups with significant differences in OS on Kaplan-Meier analysis ( Conclusion: Here, we designed and validated a prognostic nomogram for OS in postoperative LSCC patients. Application of our model in the clinical setting may assist clinicians in evaluating patient prognosis and providing highly individualized therapy.

Indexed as

end results (SEER)epidemiologylung squamous cell carcinoma (LSCC)nomogramprognosissurgerysurveillance

Identifiers

PMID37091268
PMCPMC10118027

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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.