Evidence map›Paper›PMID 32489303›Full record

ArticleSaudi journal of biological sciences2020

Prospective breast cancer risk factors prediction in Saudi women.

Sawsan Babiker, Omaima Nasir, S H Alotaibi, Alaa Marzogi, Mohammad Bogari, Tahani Alghamdi

Abstract read
In one paragraph

Article in Saudi journal of biological sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
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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

6 authors.

Sawsan BabikerDepartment of Mathematics, Turabah University College, Taif University, Saudi Arabia.
Omaima NasirDepartment of Biology, Turabah University College, Taif University, Saudi Arabia.
S H AlotaibiDepartment of Chemistry, Turabah University College, Taif University, Saudi Arabia.
Alaa MarzogiKing Abd Alla Medical Centre, Mecca, Saudi Arabia.
Mohammad BogariKing Abd Alla Medical Centre, Mecca, Saudi Arabia.
Tahani AlghamdiKing Abd Alla Medical Centre, Mecca, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Women's health is affected by breast cancer worldwide and Saudi Arabia (SA) is no exception. Malignancy has enormous consequences for social, psychological and public health. The aim of this study was to examine the risk factors for Saudi women from breast cancer using logistic regression models. In 135 patient cases for different stages of breast cancer was used to study case management, 270 healthy women from King Abd Alla Medical City, Mecca, SA were taken to predict the probability of women developing breast cancer, logistic regression was analyzed taking factors such as age, marital status, family history, parity, age at first full-term pregnancy, menopausal status, body mass index (BMI) and breast feeding. The logistic regression model showed that there are important risk factors (age, marital status, family history, parity, age at first full-term pregnancy, menopausal status, body mass index, and breast feeding) in development of breast cancer. Fewer cases were observed in unmarried women, age ≤30, BMI ≤20. In contrast, more cases were found with women age 41-50 married, BMI > 30, a smaller number of children, not breast feeding, age of first pregnancy ≥30, menopausal status age at 46-50. Based on our data there is role of risk factors in developing breast cancer, less BMI, the increase number of children, breast feeding, which are playing as protective factor for breast cancer.

Indexed as

Breast cancerLogistic RegressionRisk FactorsSaudi women

Identifiers

PMID32489303
PMCPMC7254039

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LicenceCC BY-NC-ND
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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.