Evidence map›Paper›PMID 38645854›Full record

ArticleSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2024

[Construction and Validation of Prediction Models of Risk Factors for Early Death in Patients With Metastatic Melanoma].

Siru Li, Jing Li, Qi Yang, Cunli Yin, Bin Liu

Open access · greenAbstract readValidation StudyEnglish Abstract
In one paragraph

Article in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 40% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

5 authors at 1 institution in 1 country.

Siru Li( 610054) Medical School of University of Electronic Science and Technology of China, Chengdu 610054, China.
Jing Li( 610054) Medical School of University of Electronic Science and Technology of China, Chengdu 610054, China.
Qi Yang( 610054) Medical School of University of Electronic Science and Technology of China, Chengdu 610054, China.
Cunli Yin( 610054) Medical School of University of Electronic Science and Technology of China, Chengdu 610054, China.
Bin Liu( 610054) Medical School of University of Electronic Science and Technology of China, Chengdu 610054, China.
University of Electronic Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To construct nomogram models to predict the risk factors for early death in patients with metastatic melanoma (MM). Methods: The study covered 2138 cases from the Surveillance, Epidemiology, and End Results Program (SEER) database and all these patients were diagnosed with MM between 2010 and 2015. Logistic regression was performed to identify independent risk factors affecting early death in MM patients. These risk factors were then used to construct nomograms of all-cause early death and cancer-specific early death. The efficacy of the model was assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). In addition, external validation of the model was performed with clinicopathologic data of 105 patients diagnosed with MM at Sichuan Cancer Hospital between January 2015 and January 2020. Results: According to the results of logistic regression, marital status, the primary site, N staging, surgery, chemotherapy, bone metastases, liver metastases, lung metastases, and brain metastases could be defined as independent predictive factors for early death. Based on these factors, 2 nomograms were plotted to predict the risks of all-cause early death and cancer-specific early death, respectively. For the models for all-cause and cancer-specific early death, the areas under the curve ( Conclusion: The nomogram models demonstrated good performance in predicting early death in MM patients and can be used to help clinical oncologists develop more individualized treatment strategies.

Indexed as

DeathMelanomaNeoplasm MetastasisNomogramsAdultAgedArea Under CurveBone NeoplasmsCarcinoma, HepatocellularFemaleHumansLogistic ModelsLung NeoplasmsMiddle AgedModels, StatisticalMelanomaNeoplasm metastasisNomograms modelsSEER databaseStatistical early death

Identifiers

PMID38645854
PMCPMC11026897
OpenAlexW4395009387

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.