Evidence map›Paper›PMID 31983179›Full record

ArticleAsian Pacific journal of cancer prevention : APJCP2020

An Overview of Lung and Breast Cancer Using the National Cancer Database.

Leigh Deshotels, Gerard Chaaya, Takefumi Komiya

Abstract read
In one paragraph

Article in Asian Pacific journal of cancer prevention : APJCP, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Leigh DeshotelsDepartment of Internal Medicine, School of Medicine, Tulane University, 1430 Tulane Ave., New Orleans, LA, USA.
Gerard ChaayaSection of Hematology and Medical Oncology School of Medicine , Tulane University, 1430 Tulane Ave., New Orleans, LA, USA.
Takefumi KomiyaParkview Health, Parkview Cancer Institute, 11050 Parkview Circle, Fort Wayne, IN, 46845, U S A.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe National Cancer Database (NCDB) is a clinical oncology database utilized by many researchers and clinicians internationally. We sought to investigate the various trends in data of two of the most common cancers, breast and lung, published using the NCDB. MATERIALS AND

methodsWe selected a multitude of pre-determined variables for analysis. We then performed two separate literature searches using an advanced PubMed search builder, and the data was combined to determine each variables' association with journal impact factor (IF) using both univariate and multivariate analyses.

resultsA total of 191 published studies were identified. We found that a journal IF > 5 was associated with a publication year prior to 2017 (univariate analysis OR 2.68, 95% CI 1.38-5.21, p-value 0.004 and multivariate analysis OR 3.47, 95% CI 1.62-7.42, p-value 0.001) and a sample size > 10,000 (univariate analysis OR 3.27, 95% CI 1.43-7.50, p-value 0.005 and multivariate analysis OR 4.68, 95% CI 1.89-11.6, p-value 0.0008). Variables such as number of authors, region, cancer type, stage, treatment outcome and treatment incidence were not significant for an association with an IF >5.

conclusionBased on our data, studies published after 2017 using the NCDB were associated with a lower IF. This could suggest that the quality of the NCDB data may be declining over time, or NCDB is becoming more widely used.<br />.

Indexed as

Databases, FactualJournal Impact FactorBreast NeoplasmsFemaleHumansLung NeoplasmsPeriodicals as Topicbreast cancerLung cancerNational Cancer Databasenational registry data

Identifiers

PMID31983179
PMCPMC7294020

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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.