Breast Cancer Mortality in Relation to Clinical Breast Examination and Breast Self-Examination
Bibliographic record
Abstract
Mammography is the most sensitive available means for early detection of breast cancer, but both clinical breast examination (CBE) and breast self-examination (BSE) have the potential to advance the diagnosis of breast cancer without the expense of a mammography facility. CBE detects about 60% of cancers detected by mammography, as well as some cancers not detected by mammography. There have been no randomized trials comparing breast cancer mortality between women offered and not offered CBE. However, indirect evidence comes from a Canadian study in which women were randomly assigned to CBE alone or CBE plus mammography. Women in the two groups had similar rates of nodal involvement at diagnosis and of breast cancer mortality. Thus if receipt of mammography averts some deaths from breast cancer, the results of this study suggest that CBE has the potential to do so as well. Most studies have found that breast cancers detected by BSE are smaller than those detected without screening and are more likely to be confined to the breast; furthermore, survival after a diagnosis of breast cancer tends to be longer among women who practice BSE than among women who do not. However, neither observational nor randomized studies of BSE provide evidence that this screening modality reduces breast cancer mortality. A recent randomized study in Shanghai, China, found that women assigned to extensive BSE instruction and women assigned to another health intervention had similar distributions of cancer size and stage at diagnosis and similar breast cancer mortality rates. In summary, CBE appears to be a promising means of averting some deaths from breast cancer, whereas BSE appears to have little or no impact on breast cancer mortality.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".