It Is Time to Initiate Another Breast Cancer Screening Trial
Bibliographic record
Abstract
Ideas and Opinions17 June 2014It Is Time to Initiate Another Breast Cancer Screening TrialPeter Jüni, MD and Marcel Zwahlen, PhDPeter Jüni, MDFrom Institute of Social and Preventive Medicine, University of Bern, Switzerland.Search for more papers by this author and Marcel Zwahlen, PhDFrom Institute of Social and Preventive Medicine, University of Bern, Switzerland.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M14-0569 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail In 2009, the U.S. Preventive Services Task Force triggered a heated debate by recommending biennial mammography screening for women aged 50 to 74 years and individualized decisions about mammography for younger women based on context and personal preferences (1). The debate calmed somewhat in 2012 when the Independent U.K. Panel on Breast Cancer Screening concluded that, although associated with some overdiagnosis, screening mammography reduced breast cancer mortality (2). Recent reports have refueled controversy, including a technology assessment issued by the Swiss Medical Board that did not recommend mammography screening regardless of age (3) and the 25-year follow-up of the 2 ...References1. U.S. Preventive Services Task Force. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2009;151:716-26. [PMID: 19920272] LinkGoogle Scholar2. Independent UK Panel on Breast Cancer Screening. The benefits and harms of breast cancer screening: an independent review. Lancet. 2012;380:1778-86. [PMID: 23117178] CrossrefMedlineGoogle Scholar3. Swiss Medical Board. Systematic mammography screening. Zollikon, Switzerland: Swiss Medical Board; 2013. Accessed at www.medical-board.ch/fileadmin/docs/public/mb/Fachberichte/2013-12-15_Bericht_Mammographie_Final_Kurzfassung_e.pdf on 3 February 2014. Google Scholar4. Miller AB, Wall C, Baines CJ, Sun P, To T, Narod SA. Twenty five year follow-up for breast cancer incidence and mortality of the Canadian National Breast Screening Study: randomised screening trial. BMJ. 2014;348:g366. [PMID: 24519768] CrossrefMedlineGoogle Scholar5. Gøtzsche PC, Jørgensen KJ. Screening for breast cancer with mammography. Cochrane Database Syst Rev. 2013;6:CD001877. [PMID: 23737396] MedlineGoogle Scholar6. Jüni P, Altman DG, Egger M. Systematic reviews in health care: Assessing the quality of controlled clinical trials. BMJ. 2001;323:42-6. [PMID: 11440947] CrossrefMedlineGoogle Scholar7. Black WC, Haggstrom DA, Welch HG. All-cause mortality in randomized trials of cancer screening. J Natl Cancer Inst. 2002;94:167-73. [PMID: 11830606] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Peter Jüni, MD; Marcel Zwahlen, PhDAffiliations: From Institute of Social and Preventive Medicine, University of Bern, Switzerland.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M14-0569.Corresponding Author: Peter Jüni, MD, Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, CH-3012, Bern, Switzerland; e-mail, juni@ispm.unibe.ch.Current Author Addresses: Drs. Jüni and Zwahlen: Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, CH-3012, Bern, Switzerland.Author Contributions: Conception and design: P. Jüni, M. Zwahlen.Analysis and interpretation of the data: P. Jüni, M. Zwahlen.Drafting of the article: P. Jüni.Critical revision of the article for important intellectual content: P. Jüni, M. Zwahlen.Final approval of the article: P. Jüni, M. Zwahlen.Statistical expertise: P. Jüni, M. Zwahlen.Administrative, technical, or logistic support: P. Jüni.Collection and assembly of data: P. Jüni, M. Zwahlen.This article was published online first at www.annals.org on 8 April 2014. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byPredicting difference in mean survival time from cause-specific hazard ratios for women diagnosed with breast cancerQuality‐of‐life effects of screening mammography in NorwayHow the USPSTF's Mammographic Screening Guidelines Should Be InterpretedOverview of guidelines on breast screening: Why recommendations differ and what to do about itThe Effect of Treatment Advances on the Mortality Results of Breast Cancer Screening Trials: A Microsimulation ModelJeanette Birnbaum, PhD, MPH, Vijayakrishna K. Gadi, MD, PhD, Elan Markowitz, and Ruth Etzioni, PhDBenefits and harms of mammography screeningCorrespondance à propos de l’article « Dépistage du cancer du sein ». Des conclusions… aux actes ! 17 June 2014Volume 160, Issue 12 Page: 864-866 Keywords Breast cancer Breast cancer screening Cancer screening Clinical trial reporting Disclosure Interquartile range Mammography Mortality Preventive medicine Relative risk ePublished: 17 June 2014 Issue Published: 17 June 2014 Copyright & PermissionsCopyright © 2014 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.034 | 0.021 |
| Insufficient payload (model declined to judge) | 0.110 | 0.059 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".