Evidence map›Paper›PMID 29172267›Full record

ArticleAsian Pacific journal of cancer prevention : APJCP2017

Breast Cancer Risk Assessment Using the Gail Model and It’s Predictors in Saudi Women

Hala Hazam Al Otaibi

Abstract read
In one paragraph

Article in Asian Pacific journal of cancer prevention : APJCP, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.2field-weighted citation impact, top 40% of its field
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

10 citing papers in PubMed, 12 citations in OpenAlex.

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  8. Review
  9. Use of Receiver Operating Characteristic (ROC) Curve Analysis for Tyrer-Cuzick and Gail in Breast Cancer Screening in Jiangxi Province, China.Medical science monitor : international medical journal of experimental and clinical research · 2018
    Article
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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

1 author at 1 institution in 1 country.

Hala Hazam Al OtaibiDepartment of Food Sciences and Nutrition, College of Agriculture and Food Science, King Faisal University, Saudi Arabia. Email: hhalotaibi@kfu.edu.sa
King Faisal University · SA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The Gail Model has been widely implemented in developed and developing countries and is considered to be the best available instrument to estimate breast cancer (BC) risk for early prevention. Objective: The goals of the study were to determine five-year and lifetime BC risks and to assess BC predictors among female Saudi teachers using the Gail model. Methods: A cross sectional study with convenience sampling was conducted among 180 female Saudi secondary school teachers. The Gail model was used to evaluate the five-year and lifetime risks of developing BC. Included were a one-day 24-hour recall to assess daily serving sizes and food groups for food intake and questions regarding daily exercise, BMI, and demographic data. Result: The mean age of the teachers was 41±7.2 years, with a 0.87±0.93 mean for the five-year risk and a 9.6±5.4 mean lifetime risk of developing BC. According to the general linear model, the BC risk predictors were age, age at menarche, age at first pregnancy, family history, BMI, fruit and vegetable intake, and meat intake. Conclusion: The present study provided new information regarding the potential factors for five-year and lifetime invasive BC risk among Saudi women. Moreover, we could confirm that the Gail model is an appropriate BC risk assessment tool for Saudi women for early prevention, particularly among women at high risk of BC.

Indexed as

Breast cancerdietfive-year riskGail modellifestylelifetime risk

Identifiers

PMID29172267
PMCPMC5773779
OpenAlexW2767929955

What OpenQuestion holds

Textmetadata
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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.