Clinical Breast Examination: Practical Recommendations for Optimizing Performance and Reporting
Bibliographic record
Abstract
Clinical breast examination (CBE) seeks to detect breast abnormalities or evaluate patient reports of symptoms to find palpable breast cancers at an earlier stage of progression. Treatment options for earlier-stage cancers are generally more numerous, include less toxic alternatives, and are usually more effective than treatments for later-stage cancers. For average-risk women aged 40 and younger, earlier detection of palpable tumors identified by CBE can lead to earlier therapy. After age 40, when mammography is recommended, CBE is regarded as an adjunct to mammography. Recent debate, however, has questioned the contributions of CBE to the detection of breast cancer in asymptomatic women and particularly to improved survival and reduced mortality rates. Clinicians remain widely divided about the level of evidence supporting CBE and their confidence in the examination. Yet, CBE is practiced extensively in the United States and continues to be recommended by many leading health organizations. It is in this context that this report provides a brief review of evidence for CBE's role in the earlier detection of breast cancer, highlights current practice issues, and presents recommendations that, when implemented, could contribute to greater standardization of the practice and reporting of CBE. These recommendations may also lead to improved evidence of the nature and extent of CBE's contribution to the earlier detection of breast cancer.
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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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.020 |
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".