Evidence map›Paper›PMID 26752291›Full record

ArticlePloS one2016

Development and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer.

Ami Yu, Sang Myung Woo, Jungnam Joo, Hye-Ryung Yang, Woo Jin Lee, Sang-Jae Park, Byung-Ho Nam

Abstract readValidation Study
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Early detection of pancreatic cancer.The lancet. Gastroenterology & hepatology · 2020
    Review
  7. Article
  8. Article
  9. Environmental Risk Factors of Pancreatic Cancer.Journal of clinical medicine · 2019
    Review
  10. Review
  11. Pancreatic Cancer Prediction Through an Artificial Neural Network.Frontiers in artificial intelligence · 2019
    Article
  12. Article
  13. Article
  14. 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

7 authors.

Ami YuBiometric Research Branch, Division of Cancer Epidemiology and Prevention, Research Institute & Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.
Sang Myung WooCenter for Liver Cancer, Research Institute & Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.
Jungnam JooBiometric Research Branch, Division of Cancer Epidemiology and Prevention, Research Institute & Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.
Hye-Ryung YangBiometric Research Branch, Division of Cancer Epidemiology and Prevention, Research Institute & Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.
Woo Jin LeeCenter for Liver Cancer, Research Institute & Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.
Sang-Jae ParkCenter for Liver Cancer, Research Institute & Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.
Byung-Ho NamBiometric Research Branch, Division of Cancer Epidemiology and Prevention, Research Institute & Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThere is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women. MATERIALS AND

methodsGender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χ2 statistic.

resultsA total of 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability.

conclusionGender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer.

Indexed as

Proportional Hazards ModelsAdultAgedAged, 80 and overAge DistributionCohort StudiesFemaleHumansMaleMiddle AgedPancreatic NeoplasmsProspective StudiesRepublic of KoreaRisk FactorsSmoking

Identifiers

PMID26752291
PMCPMC4708985

What OpenQuestion holds

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LicenceCC BY
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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.