Evidence map›Paper›PMID 39444025›Full record

ArticleBMC proceedings2024

Risk stratification in breast screening workshop.

Andrew Anderson, Cristina Visintin, Antonis Antoniou, Nora Pashayan, Fiona J Gilbert, Allan Hackshaw, Rikesh Bhatt, Harry Hill, Stuart Wright, Katherine Payne and 4 more

Abstract read
In one paragraph

Article in BMC proceedings, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Andrew AndersonUK NSC secretariat, Sheffield/London, UK. Andrew.Anderson@dhsc.gov.uk.
Cristina VisintinUK NSC secretariat, Sheffield/London, UK.
Antonis AntoniouUniversity of Cambridge, Cambridge, UK.
Nora PashayanUniversity of Cambridge, Cambridge, UK.
Fiona J GilbertUniversity of Cambridge, Cambridge, UK.
Allan HackshawUniversity College London (UCL), London, UK.
Rikesh BhattUniversity College London (UCL), London, UK.
Harry HillThe University of Sheffield, Sheffield, UK.
Stuart WrightThe University of Manchester, Manchester, UK.
Katherine PayneThe University of Manchester, Manchester, UK.
Gabriel RogersThe University of Manchester, Manchester, UK.
Bethany ShinkinsThe University of Warwick, Coventry, UK.
Sian Taylor-PhillipsThe University of Warwick, Coventry, UK.
Rosalind Given-WilsonSt George's Healthcare NHS Foundation Trust, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Population screening for breast cancer (BC) is currently offered in the UK for women aged 50 to 71 with the aim of reducing mortality. There is additional screening within the national programme for women identified as having a very high risk of BC. There is growing interest in further risk stratification in breast screening, which would require a whole population risk assessment and the subsequent offer of screening tailored to the individual's risk. Some women would be offered more intensive screening than others or no screening. This might provide a better balance of screening benefits and harms for each individual than the current population age-based programme alone. The UK National Screening Committee (UK NSC) is considering using decision-analytic and other models to evaluate different risk stratification screening strategies and identify remaining gaps in evidence. This paper reports the proceedings of a UK NSC workshop where experts in the field discussed both risk prediction models, as well as decision-analytic models providing a benefit-harm analysis/economic evaluation of risk-stratified screening programmes (see Table 1). The aim of the meeting was to present and discuss the current work of experts, including some data which had not been published at the time of the meeting, to inform the UK NSC. The workshop was not intended to present a balanced evaluation of how to deliver screening in future. Areas for further work identified included methods for comparing models to assess accuracy, the optimum risk assessment tools, the digital screening infrastructure, acceptability of stratification, choice of screening test and reducing inequalities. A move to risk stratification of the whole programme would require a careful phased introduction with continuing assessment of real-world evidence during deployment.

Indexed as

Breast cancerCost-effectivenessEpidemiologyRisk stratificationScreening

Identifiers

PMID39444025
PMCPMC11500431

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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.