Bibliographic record
Abstract
The most convincing evidence that a factor such as dietary fat is causally related to breast cancer would be obtained from a randomised controlled trial in which exposure to dietary fat intake was systematically varied. A limitation of randomised controlled trials of breast cancer prevention, however, is the large sample size required to detect plausible reductions in risk resulting from the intervention. We describe here experience over a period of 9 years with the use of one risk factor for breast cancer as a criterion for entry to a clinical trial of breast cancer prevention. The risk factor used was the presence of extensive densities in the breast tissue on mammography, which has been found by several investigators to be strongly associated with risk of breast cancer. Using this criterion for selection, 1800 subjects of mean age 46 years were enrolled between 1982 and 1986, and again between 1988 and the present. Throughout this period, the point estimate of annual invasive cancer incidence was approximately 6 per 1000 per year. The observed cancer incidence has been consistently 4-5 times the incidence expected from age-specific breast cancer incidence data for women living in Ontario. These data show that the selection of subjects for a clinical trial of breast cancer prevention using the criterion of extensive breast parenchymal densities does identify a group at substantially increased risk of breast cancer. Use of this criterion for the selection of subjects can substantially reduce the sample size required for a clinical trial of a preventive strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".