Evidence map›Paper›PMID 36981513›Full record

ArticleHealthcare (Basel, Switzerland)2023

A Clinical Prediction Model for Breast Cancer in Women Having Their First Mammogram.

Piyanun Wangkulangkul, Suphawat Laohawiriyakamol, Puttisak Puttawibul, Surasak Sangkhathat, Varanatjaa Pradaranon, Thammasin Ingviya

Full text read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Temporal trend of breast cancer burden among younger and older Brazilian women, 1990-2019.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2025
    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

6 authors.

Piyanun WangkulangkulDepartment of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.ORCID 0000-0002-0585-3649
Suphawat LaohawiriyakamolDepartment of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.
Puttisak PuttawibulDepartment of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.
Surasak SangkhathatDepartment of Surgery, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.ORCID 0000-0003-3622-3233
Varanatjaa PradaranonDivision of Radiology, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.
Thammasin IngviyaDepartment of Family and Preventive Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.ORCID 0000-0002-8894-3817

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital mammography is the most efficient screening and diagnostic modality for breast cancer (BC). However, the technology is not widely available in rural areas. This study aimed to construct a prediction model for BC in women scheduled for their first mammography at a breast center to prioritize patients on waiting lists.

methodsThis retrospective cohort study analyzed breast clinic data from January 2013 to December 2017. Clinical parameters that were significantly associated with a BC diagnosis were used to construct predictive models using stepwise multiple logistic regression. The models' discriminative capabilities were compared using receiver operating characteristic curves (AUCs).

resultsData from 822 women were selected for analysis using an inverse probability weighting method. Significant risk factors were age, body mass index (BMI), family history of BC, and indicated symptoms (mass and/or nipple discharge). When these factors were used to construct a model, the model performance according to the Akaike criterion was 1387.9, and the AUC was 0.82 (95% confidence interval: 0.76-0.87).

conclusionIn a resource-limited setting, the priority for a first mammogram should be patients with mass and/or nipple discharge, asymptomatic patients who are older or have high BMI, and women with a family history of BC.

Indexed as

breast cancer risksbreast cancer screeningmammographyprediction model

Identifiers

PMID36981513
PMCPMC10048653

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read14
Read underepoch 390

Registered trials

None linked

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.