Evidence map›Paper›PMID 25001493›Full record

ArticleBMC endocrine disorders2014

Clinical, pathological and prognostic characteristics of gastroenteropancreatic neuroendocrine neoplasms in China: a retrospective study.

Xianbin Zhang, Li Ma, Haidong Bao, Jing Zhang, Zhongyu Wang, Peng Gong

Abstract read
In one paragraph

Article in BMC endocrine disorders, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 1 pooled it
–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

34 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Xianbin Zhang
Li Ma
Haidong Bao
Jing Zhang
Zhongyu Wang
Peng GongDepartment of Hepatobiliary Surgery, the First Affiliated Hospital of Dalian Medical University, Zhongshan Road No, 222, Dalian 116011, Liaoning Province, China. gongpengdalian@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are rare neuroendocrine tumors, and lack of data in Asian populations especially in China. The aim of this retrospective study was to assess the clinical, pathological and prognostic characteristics of GEP-NENs in China.

methodsWe collected clinical and pathological data of 168 patients diagnosed with GEP-NENs and treated at the First and Second Affiliated Hospitals of Dalian Medical University between January 2003 and December 2012. Kaplan-Meier method and log rank analysis was used to analyze the prognostic significance of clinical and pathological characteristics.

resultsMean age was 51.83 ± 14.03 and the male-to-female ratio was 1.5:1. Primary sites were the rectum (58.93%), pancreas (13.69%), stomach (9.52%), duodenum (5.36%), colon (4.76%), appendix (4.76%), ileum (2.38%) and jejunum (0.60%). Most patients (95.83%) presented non-functional tumors with non-specific symptoms such as abdominal or back pain (29.17%) and gastrointestinal bleeding (25.60%). Based on the 2010 World Health Organization (WHO) classification, patients were diagnosed with neuroendocrine tumor (NET) (24.40%) or neuroendocrine carcinoma (NEC) (7.14%). The estimated mean survival was 8.94 ± 0.28 years (95% CI: 8.40-9.48). Male gender, young age, small tumor size and NET tumor type were favorable prognostic factors.

conclusionChinese GEP-NENs patients present characteristics that are similar to American and European patients. However, there is an urgent need to establish a national database for understanding the clinical and epidemiological features of GEP-NENs in China.

Indexed as

ChinaCombined Modality TherapyFemaleFollow-Up StudiesHumansIntestinal NeoplasmsMaleMiddle AgedNeoplasm StagingNeuroendocrine TumorsPancreatic NeoplasmsPrognosisRetrospective StudiesStomach Neoplasms

Identifiers

PMID25001493
PMCPMC4107480

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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.