Evidence map›Paper›PMID 33784268›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2021

Prognostic Nomograms to Predict Survival of Patients with Resectable Gallbladder Cancer: A Surveillance, Epidemiology, and End Results (SEER)-Based Analysis.

Yan Lin, Hua Chen, Fan Pan

Abstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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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3 · Its place in the literature

Who cites it

6 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

3 authors.

Yan LinDepartment of Gastroenterology, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, Fujian, China (mainland).
Hua ChenDepartment of General Surgery, Ningde Medical District, 900th Hospital of the Joint Logistics Team, People's Liberation Army (PLA), Ningde, Fujian, China (mainland).
Fan PanDepartment of Hepatobiliary Surgery, 900th Hospital of the Joint Logistics Team, People's Liberation Army (PLA), Fuzhou, Fujian, China (mainland).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Gallbladder adenocarcinoma (GBAC) is globally acknowledged as one of the most common malignancies among all gastrointestinal cancers. Despite prognosis of GBAC patients remains poor, patients with early-stage disease can be observed with long-term survival. MATERIAL AND METHODS In this study, 2556 patients with pathological GBAC between 2010 and 2015 were derived from the Surveillance, Epidemiology, and End Results (SEER) database. The prognostic nomograms containing all independent prognostic factors for predicting overall survival (OS) and cancer-specific survival (CSS) were constructed to achieve superior prognostic discriminatory ability. RESULTS Based on the AJCC 7th TNM staging system, we found the TNM substaging was not accurate enough to predict the survival and stratify the risk. Based on the results of univariate and multivariate analyses, a more precise prognostic nomogram was constructed containing all significant independent prognostic factors (age, grade, TNM stage, bone metastasis, and chemotherapy) for OS, while age, grade, TNM stage, bone metastasis and radiotherapy significant independent prognostic factors for CSS. The C-index of the constructed nomogram for predicting OS and CSS was 0.740 and 0.737 higher than that of TNM staging alone (0.667 for OS and 0.689 for CSS), respectively. In addition, the calibration curves and decision curve analysis further showed its robust power in survival prediction. CONCLUSIONS The constructed nomograms showed better discrimination abilities to predict OS and CSS rates at 1, 3, and 5 years. In the future, these constructed models for this disease will assist in risk stratification to guide GBAC treatment.

Indexed as

NomogramsAdenocarcinomaAgedAged, 80 and overChinaFemaleGallbladder NeoplasmsHumansMaleMiddle AgedNeoplasm MetastasisNeoplasm StagingPrognosisRiskSurvival Analysis

Identifiers

PMID33784268
PMCPMC8019267

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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.