Evidence map›Paper›PMID 32409594›Full record

ArticleCancer prevention research (Philadelphia, Pa.)2020

Utilizing Cultural and Ethnic Variables in Screening Models to Identify Individuals at High Risk for Gastric Cancer: A Pilot Study.

Haejin In, Ian Solsky, Philip E Castle, Clyde B Schechter, Michael Parides, Patricia Friedmann, Judith Wylie-Rosett, M Margaret Kemeny, Bruce D Rapkin

Abstract read
In one paragraph

Article in Cancer prevention research (Philadelphia, Pa.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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, 1 synthesis or guideline pooled it.

  1. Diagnostic Risk Prediction Models for Upper Gastrointestinal Cancers: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
    Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Risk Prediction Models for Gastric Cancer: A Scoping Review.Journal of multidisciplinary healthcare · 2024
    Article
  8. 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

9 authors.

Haejin InDepartment of Surgery, Montefiore Medical Center, New York, New York. hin@montefiore.org.
Ian SolskyDepartment of Surgery, Montefiore Medical Center, New York, New York.
Philip E CastleDepartment of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, New York.ORCID 0000-0003-1082-6554
Clyde B SchechterDepartment of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, New York.ORCID 0000-0003-0010-2827
Michael ParidesDepartment of Surgery, Montefiore Medical Center, New York, New York.
Patricia FriedmannDepartment of Surgery, Montefiore Medical Center, New York, New York.
Judith Wylie-RosettDepartment of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, New York.
M Margaret KemenyNYC Health+Hospitals/Queens, Queens, New York.
Bruce D RapkinDepartment of Epidemiology & Population Health, Albert Einstein College of Medicine, New York, New York.

Funding

Collaborations and NCORP Collective ManagementUG1CA189823 · NCI · MAYO CLINIC ROCHESTER · PI Evanthia Galanis, Olwen Hahn · 2014 to 2026
$120.6M
Montefiore Academic Community NCORP ProgramUG1CA189859 · NCI · MONTEFIORE MEDICAL CENTER (BRONX, NY) · PI Balazs Halmos, Della Makower · 2014 to 2026
$10.8M
NCI NIH HHS UG1 CA189823NCI NIH HHS UG1 CA189859
6 · The paper itself

Abstract

Identifying persons at high risk for gastric cancer is needed for targeted interventions for prevention and control in low-incidence regions. Combining ethnic/cultural factors with conventional gastric cancer risk factors may enhance identification of high-risk persons. Data from a prior case-control study (40 gastric cancer cases and 100 controls) were used. A "conventional model" using risk factors included in the Harvard Cancer Risk Index's gastric cancer module was compared with a "parsimonious model" created from the most predictive variables of the conventional model as well as ethnic/cultural and socioeconomic variables. Model probability cutoffs aimed to identify a cohort with at least 10 times the baseline risk using Bayes' Theorem applied to baseline U.S. gastric cancer incidence. The parsimonious model included age, U.S. generation, race, cultural food at ages 15-18 years, excessive salt, education, alcohol, and family history. This 11-item model enriched the baseline risk by 10-fold, at the 0.5 probability level cutoff, with an estimated sensitivity of 72% [95% confidence interval (CI), 64-80], specificity of 94% (95% CI, 90-97), and ability to identify a subcohort with gastric cancer prevalence of 128.5 per 100,000. The conventional model was only able to reach a risk level of 9.8 times baseline with a corresponding sensitivity of 31% (95% CI, 23-39) and specificity of 97% (95% CI, 94-99). Cultural and ethnic data may add important information to models for identifying U.S. individuals at high risk for gastric cancer, who then could be targeted for interventions to prevent and control gastric cancer. The findings of this pilot study remain to be validated in an external dataset.

Indexed as

AgedAge FactorsBayes TheoremCase-Control StudiesCultureEarly Detection of CancerEthnicityFeasibility StudiesFeeding BehaviorFemaleHumansIncidenceMaleMiddle AgedPilot ProjectsPredictive Value of Tests

Identifiers

PMID32409594
PMCPMC7415580

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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.