Will Absolute Risk Estimation for Time to Next Screen Work for an Asian Mammography Screening Population?

Peh Joo Ho, Elaine Hsuen Lim, Nur Khaliesah Binte Mohamed Ri, Mikael Hartman, Fuh Yong Wong, Jingmei Li

Abstract read

In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Peh Joo HoGenome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, Singapore 138672, Singapore.

Elaine Hsuen LimDivision of Medical Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore.

Nur Khaliesah Binte Mohamed RiSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore.

Mikael HartmanSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore.

Fuh Yong WongDivision of Radiation Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore.

Jingmei LiGenome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, Singapore 138672, Singapore.ORCID 0000-0001-8587-7511

Funding

Agency of Science, Technology and ResearchNational Research Foundation PRECISE CIP

6 · The paper itself

Abstract

Personalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model's performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580-0.628). Calibration was better for longer-term prediction horizons (E/O

Indexed as

absolute riskbreast cancercalibrationdiscriminatory abilityGail model

Identifiers

PMID37174025

DOI10.3390/cancers15092559

PMCPMC10177032

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.