Evidence map›Paper›PMID 35832647›Full record

ArticleJournal of immunology research2022

Prediction of Postoperative Survival in Young Colorectal Cancer Patients: A Cohort Study Based on the SEER Database.

Sheng Pan, Wenchao Mei, Linfei Huang, Yan'e Tao, Jing Xu, Yuelu Ruan

Abstract readMulticenter Study
In one paragraph

Article in Journal of immunology research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

6 authors.

Sheng PanPuren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430000 Hubei, China.ORCID https://orcid.org/0000-0002-8021-5380
Wenchao MeiPuren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430000 Hubei, China.
Linfei HuangPuren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430000 Hubei, China.
Yan'e TaoPuren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430000 Hubei, China.
Jing XuPuren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430000 Hubei, China.
Yuelu RuanPuren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, 430000 Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Our aim is to make accurate and robust predictions of the risk of postoperative death in young colorectal cancer patients (18-44 years old) by combining tumor characteristics with medical and demographic information about the patient. Materials and Methods: We used the SEER database to retrieve young patients diagnosed with colorectal cancer who had undergone surgery between 2010 and 2015 as the study cohort. After excluding cases with missing information, the study cohort was divided in a 7 : 3 ratio into a training dataset and a validation dataset. To assess the predictive ability of each predictor on the prognosis of colorectal cancer patients, we used two steps of Cox univariate analysis and Cox stepwise regression to screen variables, and the screened variables were included in a multifactorial Cox proportional risk regression model for modeling. The performance of the model was tested using calibration curves, decision curves, and area under the curve (AUC) for receiver operating characteristic (ROC). Results: After excluding cases with missing information ( Conclusion: We trained and validated a model using a large multicenter cohort of young colorectal cancer patients with stable and excellent performance in both training and validation datasets.

Indexed as

Colorectal NeoplasmsNomogramsAdolescentAdultCohort StudiesHumansPrognosisROC CurveYoung Adult

Identifiers

PMID35832647
PMCPMC9273412

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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.