Evidence map›Paper›PMID 37999106›Full record

ArticleCurrent oncology (Toronto, Ont.)2023

Effect of Breast Screening Regimen on Breast Cancer Outcomes: A Modeling Study.

Martin J Yaffe, James G Mainprize

Abstract read
In one paragraph

Article in Current oncology (Toronto, Ont.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Earlier screening, better outcomes? Revisiting breast cancer screening guidelines for women in their 40s.Canadian journal of public health = Revue canadienne de sante publique · 2026
    Article
  2. Article
  3. 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

2 authors.

Martin J YaffePhysical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.ORCID 0000-0002-7227-9915
James G MainprizePhysical Sciences Platform, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.ORCID 0000-0001-5479-1280

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Guidelines vary for the age at which to begin breast cancer screening and the interval between examinations. A validated computer model was used to compare estimated outcomes between various screening regimens. The OncoSim-Breast microsimulation model (Canadian Partnership Against Cancer) was used to simulate a cohort of 1.53 million Canadian women born in 1975. The effect of screening regimen on absolute breast cancer mortality rates, stage at diagnosis, number needed to be screened to avert a breast cancer death or save a life year, abnormal recall rates and negative biopsy rates was examined for unscreened women or those entering screening at age 40 or 50 and screened annually or biennially to age 74. Compared to no screening, absolute mortality reduction was 4.6 (biennial 50-74), 5.9 (biennial 40-74) and 7.9 (annual 40-74) fewer deaths per 1000 women. The absolute rate of diagnosis of advanced cancers (Stage 2, 3 and 4) falls in favor of earlier stages as the number of lifetime screens increases. Annual screening beginning at age 40 until age 74 would provide an additional reduction of 2 and 3.3 breast cancer deaths per 1000 women compared to biennial screening beginning at ages 40 and 50, respectively. There is a corresponding drop in the absolute number of Stage 2, 3 and 4 cancers diagnosed.

Indexed as

Breast NeoplasmsAdultAgedCanadaEarly Detection of CancerFemaleHumansMammographyMass ScreeningMiddle Agedbreast cancermicrosimulation modelingmortalitynumber needed to screenregimenscreeningstage at detection

Identifiers

PMID37999106
PMCPMC10670884

What OpenQuestion holds

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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.