Evidence map›Paper›PMID 41510101›Full record

ArticleTranslational cancer research2025

Examining the effectiveness of follow-up chemotherapy in large cell neuroendocrine carcinoma: special emphasis on stage T1-2N0M0 according to 9

Hongying Pan, Yin Zhang, Minjie Ying, Chongya Zhai

Abstract read
In one paragraph

Article in Translational cancer research, 2025. 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
–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

1 citing paper in PubMed.

  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

4 authors.

Hongying PanDepartment of Hematology and Medical Oncology, The First People's Hospital of Wuyi County, Jinhua, China.
Yin ZhangDepartment of Hematology and Medical Oncology, The First People's Hospital of Wuyi County, Jinhua, China.
Minjie YingDepartment of Hematology and Medical Oncology, The First People's Hospital of Wuyi County, Jinhua, China.
Chongya ZhaiDepartment of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Large cell neuroendocrine carcinoma (LCNEC) accounts for approximately 3% of lung cancers and carries a poor prognosis. For early-stage, node-negative disease classified as T1-2N0M0 by the 9 Methods: We sourced data of patients who were diagnosed with LCNEC at the T1-2N0M0 stage and had undergone surgery, focusing on the time frame from the start of 2004 to the end of 2015, using the Surveillance, Epidemiology, and End Results (SEER) database as our resource. In order to comprehensively evaluate the cancer-specific survival (CSS) and overall survival (OS) across different groups, we implemented a multi-faceted statistical approach, encompassing subgroup analyses, propensity score matching (PSM) techniques, and Kaplan-Meier (K-M) survival curves. Additionally, we employed the Cox Proportional-Hazards model to pinpoint standalone predictors of outcomes in LCNEC staged as T1-2N0M0. Results: Of the 582 T1-2N0M0 LCNEC patients studied, 473 (81%) patients underwent surgery alone. Before and after applying propensity score adjustments, we found no notable variance in OS and CSS when comparing the surgery-only cohort to the group that received adjuvant chemotherapy. Exploratory subgroup analyses suggested potential heterogeneity in treatment associations, though biological plausibility was uncertain. Cox regression identified middle tumor location, segmentectomy, age ≥65 years, and zero regional nodes examined as independent prognostic factors (P<0.05). Conclusions: According to the 9

Indexed as

adjuvant chemotherapyearly-stage lung cancerLarge cell neuroendocrine carcinoma (LCNEC)Surveillance, Epidemiology, and End Results database (SEER database)survival analysis

Identifiers

PMID41510101
PMCPMC12776227

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.