ArticleRadiology2018
Double Reading in Breast Cancer Screening: Cohort Evaluation in the CO-OPS Trial.
Article in Radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed, 38 citations in OpenAlex.
- Socioeconomic and Urban-Rural Disparities in Mammographic Tumor Burden at Breast Cancer Diagnosis: A Regional Screening Study from Eastern Romania.Diagnostics (Basel, Switzerland) · 2026Article
- Interobserver variability of recall decisions between mammography readers in the English NHS breast screening programme: A comparison of interobserver variability measures.European journal of radiology · 2026Article
- Dual-modality CAD for breast cancer screening: dealing with discordant diagnosis between mammography and tomography.Frontiers in oncology · 2026Article
- Retrospective evaluation of a CE-marked AI system, including 1,017,208 mammography screening examinations.European radiology · 2025Article
- Modeling Radiologists' Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- In simple but challenging search tasks, most errors are stochastic.Attention, perception & psychophysics · 2024Article
- Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.Journal of breast cancer · 2023Review
- Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x.Journal of breast cancer · 2022Article
- Optimising breast cancer screening reading: blinding the second reader to the first reader's decisions.European radiology · 2022Observational
- Improving reference standards for validation of AI-based radiography.The British journal of radiology · 2021Article
- Double reading in breast cancer screening: considerations for policy-making.The British journal of radiology · 2020Review
- New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence.AJR. American journal of roentgenology · 2019Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Purpose To investigate the effect of double readings by a second radiologist on recall rates, cancer detection, and characteristics of cancers detected in the National Health Service Breast Screening Program in England. Materials and Methods In this retrospective analysis, 805 206 women were evaluated through screening and diagnostic test results by extracting 1 year of routine data from 33 English breast screening centers. Centers used double reading of digital mammograms, with arbitration if there were discrepant interpretations. Information on reader decisions, with results of follow-up tests, were used to explore the effect of the second reader. The statistical tests used were the test for equality of proportions, the χ
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.