Evidence map›Paper›PMID 39130646›Full record

ArticleThe open respiratory medicine journal2024

Clinical and Bronchoscopy Assessment in Diagnosing the Histopathology Type of Primary Central Lung Tumors.

Mia Elhidsi, Jamal Zaini, Lisnawati Rachmadi, Asmarinah Asmarinah, Aria Kekalih, Noni Soeroso, Menaldi Rasmin

Abstract read
In one paragraph

Article in The open respiratory medicine journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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

7 authors.

Mia ElhidsiDepartment of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Indonesia, Indonesia.
Jamal ZainiDepartment of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Indonesia, Indonesia.
Lisnawati RachmadiDepartment of Anatomic Pathology, Faculty of Medicine, Universitas Indonesia, Indonesia.
Asmarinah AsmarinahDepartment of Biomedical Sciences, Faculty of Medicine, Universitas Indonesia, Indonesia.
Aria KekalihCommunity Medicine, Faculty of Medicine, Universitas Indonesia, Indonesia.
Noni SoerosoPulmonology and Respiratory Medicine, Faculty of Medicine, University of North Sumatra, Medan,Indonesia.
Menaldi RasminDepartment of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Indonesia, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The location and type of a tumor influence the prognosis of lung cancer. Primary Central Lung Tumors (PCLTs) are correlated with poor prognoses and certain histologic types. This study aimed to present a comprehensive exploration of clinical and bronchoscopic assessments for diagnosing the histopathology types of PCLTs and identified the factors associated with certain histologic types. Methods: This was an observational cross-sectional study of PCLTs, defined as tumors in direct contact with hilar structures or located within the inner two-thirds of the hemithorax. We gathered demographic and clinical data, as well as data on bronchoscopy assessment and histopathology type. Tumor stage, symptoms of superior vena cava syndrome, and enlargement of lymph nodes in the paratracheal and subcarinal regions were also documented. Results: Of the 895 patients, 37.87% had primary lung tumors, with 17.76% classified as PCLTs. Notably, PCLT cases exhibited a higher proportion of stage III (28.9% Conclusion: The proportion of stage III, SCC histopathological type, and intraluminal lesions was higher in patients with PCLT than in non-PCLT cases. The presence of intraluminal lesions can predict the histopathological type of SCC in patients with PCLTs.

Indexed as

BronchoscopyClinical assessmentDiagnosisHistopathologyLung tumorsPrimary central lung tumors

Identifiers

PMID39130646
PMCPMC11311725

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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.