Screening for Breast Cancer in Average-Risk Women
Bibliographic record
Abstract
Letters17 September 2019Screening for Breast Cancer in Average-Risk WomenJennifer S. Lin, MD, MCR, Reem A. Mustafa, MD, MPH, Timothy J. Wilt, MD, MPH, Carrie A. Horwitch, MD, MPH, and Amir Qaseem, MD, PhD, MHAJennifer S. Lin, MD, MCRKaiser Permanente Northwest, Portland, Oregon (J.S.L.)Search for more papers by this author, Reem A. Mustafa, MD, MPHUniversity of Kansas Medical Center, Kansas City, Kansas (R.A.M.)Search for more papers by this author, Timothy J. Wilt, MD, MPHMinneapolis VA Center for Chronic Disease Outcomes Research and University of Minnesota School of Medicine, Minneapolis, Minnesota (T.J.W.)Search for more papers by this author, Carrie A. Horwitch, MD, MPHVirginia Mason Medical Center, Seattle, Washington (C.A.H.)Search for more papers by this author, and Amir Qaseem, MD, PhD, MHAAmerican College of Physicians, Philadelphia, Pennsylvania (A.Q.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L19-0474 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We appreciate the comments from Dr. Biggs, Dr. Grimm, Dr. Lenderink-Carpenter, and Dr. Lee and colleagues. We also appreciate the opportunity that our guidance statements provide to engage in robust peer discussion. We emphasize the broad agreement among guideline groups as highlighted by our guidance statement, including for women aged 40 to 49 years (1). None of the guidelines recommends screening in women aged 40 to 45 years; the American Cancer Society suggests that starting screening at age 45 years is a weak recommendation because of the fine balance between benefits and harms and the importance of individual ...References1. Qaseem A, Lin JS, Mustafa RA, et al; Clinical Guidelines Committee of the American College of Physicians. Screening for breast cancer in average-risk women: a guidance statement from the American College of Physicians. Ann Intern Med. 2019. [PMID: 30959525] doi:10.7326/M18-2147 LinkGoogle Scholar2. Redberg RF, Grady D. False information about breast cancer screening-reply. JAMA Intern Med. 2018;178:300. [PMID: 29404614] doi:10.1001/jamainternmed.2017.7090 CrossrefMedlineGoogle Scholar3. Moss SM, Wale C, Smith R, et al. Effect of mammographic screening from age 40 years on breast cancer mortality in the UK Age trial at 17 years' follow-up: a randomised controlled trial. Lancet Oncol. 2015;16:1123-32. [PMID: 26206144] doi:10.1016/S1470-2045(15)00128-X CrossrefMedlineGoogle Scholar4. Miller AB, Wall C, Baines CJ, et al. 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] doi:10.1136/bmj.g366 CrossrefMedlineGoogle Scholar5. Nelson HD, Fu R, Cantor A, et al. Effectiveness of breast cancer screening: systematic review and meta-analysis to update the 2009 U.S. Preventive Services Task Force recommendation. Ann Intern Med. 2016;164:244-55. [PMID: 26756588]. doi:10.7326/M15-0969 LinkGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Kaiser Permanente Northwest, Portland, Oregon (J.S.L.)University of Kansas Medical Center, Kansas City, Kansas (R.A.M.)Minneapolis VA Center for Chronic Disease Outcomes Research and University of Minnesota School of Medicine, Minneapolis, Minnesota (T.J.W.)Virginia Mason Medical Center, Seattle, Washington (C.A.H.)American College of Physicians, Philadelphia, Pennsylvania (A.Q.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-2147. A record of disclosures of interest and management of conflicts of is kept for each Clinical Guidelines Committee meeting and conference call and can be viewed at www.acponline.org/clinical_information/guidelines/guidelines/conflicts_cgc.htm. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoScreening for Breast Cancer in Average-Risk Women: A Guidance Statement From the American College of Physicians Amir Qaseem , Jennifer S. Lin , Reem A. Mustafa , Carrie A. Horwitch , Timothy J. Wilt , and Screening for Breast Cancer in Average-Risk Women Lars J. Grimm Screening for Breast Cancer in Average-Risk Women Amanda Lenderink-Carpenter Screening for Breast Cancer in Average-Risk Women Michelle V. Lee , Debbie L. Bennett , and Catherine M. Appleton Screening for Breast Cancer in Average-Risk Women Kelly W. Biggs Metrics 17 September 2019Volume 171, Issue 6Page: 451-452KeywordsBreast cancerBreast cancer screeningCancer screeningDisclosureLife expectancyMorbidityMortalityRacial and ethnic issuesRadiation exposureScreening guidelines ePublished: 17 September 2019 Issue Published: 17 September 2019 Copyright & PermissionsCopyright © 2019 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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.011 |
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".