Evidence map›Paper›PMID 34806493›Full record

ArticleTechnology in cancer research & treatment

The Clinicopathological Features and Overall Survival of Patients With Gastric Neuroendocrine Carcinoma.

Bin Xu, Yuxin Chu, Qinyong Hu, Qibin Song

Open access · goldAbstract read
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Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 15 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Bin XuCancer Center, Renmin Hospital of Wuhan University, Wuhan, China.
Yuxin ChuCancer Center, Renmin Hospital of Wuhan University, Wuhan, China.ORCID 0000-0001-5526-997X
Qinyong HuCancer Center, Renmin Hospital of Wuhan University, Wuhan, China.
Qibin SongCancer Center, Renmin Hospital of Wuhan University, Wuhan, China.
Wuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesGastric neuroendocrine carcinoma (GNEC) is a class of rare histological subtypes in gastric cancer (GC). This retrospective case-control study aimed to explore the clinicopathological features and overall survival (OS) of patients with GNEC.

methodsA large population of GNEC and intestinal-type GC (IGC) patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The 1:1 propensity score matching (PSM) analysis was initiated to adjust the confounders between GNEC and IGC cohorts. Kaplan-Meier (KM) plots with log-rank tests were used to compare the survival differences in GNEC versus IGC. Additionally, Cox proportional hazard regression models were adopted to characterize the prognostic factors relevant to OS of the GNEC patients.

resultsAn entity of 4596 patients were collected, including 3943 (85.8%) IGC patients and 653 (14.2%) GNEC patients. The PSM analysis well-balanced all confounders in GNEC versus IGC (all

conclusionsGNEC has unique clinicopathological features quite different from IGC and may have a superior survival to IGC in early stage patients. The prognostic factors identified here may assist the clinicians to more individually treat these patients.

Indexed as

AgedAged, 80 and overCarcinoma, NeuroendocrineCase-Control StudiesCombined Modality TherapyFemaleHumansKaplan-Meier EstimateMaleMiddle AgedNeoplasm GradingNeoplasm StagingPrognosisRetrospective StudiesSEER ProgramStomach Neoplasmsgastric neuroendocrine carcinomaintestinal-type gastric canceroverall survivalpropensity score matching

Identifiers

PMID34806493
PMCPMC8606720
OpenAlexW3216084088

What OpenQuestion holds

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Registered trials

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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.