Bibliographic record
Abstract
Letters2 November 2010Screening for Breast CancerHeidi D. Nelson, MD, MPH and Benjamin K. Chan, MSHeidi D. Nelson, MD, MPHFrom Oregon Evidence-based Practice Center, Oregon Health & Science University, Portland, OR 97239-3098.Search for more papers by this author and Benjamin K. Chan, MSFrom Oregon Evidence-based Practice Center, Oregon Health & Science University, Portland, OR 97239-3098.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-153-9-201011020-00017 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We agree with Dr. Saika and colleagues that adjustment for follow-up time when estimating the NNI to mammography screening to prevent 1 breast cancer death provides more comparable results across age groups. However, the purpose of our meta-analysis was primarily to determine the effectiveness of mammography screening in reducing breast cancer mortality among women in their 40s, not to determine differences between age groups. Estimates of NNI are a way to illustrate magnitudes of effect that may be more relevant to clinical applications than RRs for some audiences. These estimates were calculated in the 2002 review (1), and ...References1. Humphrey LL, Helfand M, Chan BK, Woolf SH. Breast cancer screening: a summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med. 2002;137:347-60. [PMID: 12204020] LinkGoogle Scholar2. Mandelblatt JS, Cronin KA, Bailey S, Berry DA, de Koning HJ, Draisma G, et al; Breast Cancer Working Group of the Cancer Intervention and Surveillance Modeling Network. Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms. Ann Intern Med. 2009;151:738-47. [PMID: 19920274] LinkGoogle Scholar3. 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 Scholar4. Miller AB, To T, Baines CJ, Wall C. Canadian National Breast Screening Study-2: 13-year results of a randomized trial in women aged 50-59 years. J Natl Cancer Inst. 2000;92:1490-9. [PMID: 10995804] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From Oregon Evidence-based Practice Center, Oregon Health & Science University, Portland, OR 97239-3098.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoEffects of Mammography Screening Under Different Screening Schedules: Model Estimates of Potential Benefits and Harms Jeanne S. Mandelblatt , Kathleen A. Cronin , Stephanie Bailey , Donald A. Berry , Harry J. de Koning , Gerrit Draisma , Hui Huang , Sandra J. Lee , Mark Munsell , Sylvia K. Plevritis , Peter Ravdin , Clyde B. Schechter , Bronislava Sigal , Michael A. Stoto , Natasha K. Stout , Nicolien T. van Ravesteyn , John Venier , Marvin Zelen , Eric J. Feuer , and Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation StatementScreening for Breast Cancer: An Update for the U.S. Preventive Services Task Force Heidi D. Nelson , Kari Tyne , Arpana Naik , Christina Bougatsos , Benjamin K. Chan , and Linda Humphrey Metrics 2 November 2010Volume 153, Issue 9Page: 619KeywordsAge groupsBreast cancerCancer screeningConflicts of interestDeath ratesInformation storage and retrievalMammographyStatistical models ePublished: 2 November 2010 Issue Published: 2 November 2010 Copyright & PermissionsCopyright © 2010 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.157 | 0.057 |
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".