Constructing “High-Risk Women”: The Development and Standardization of a Breast Cancer Risk Assessment Tool
Bibliographic record
Abstract
Recently, two prescription drugs (tamoxifen and raloxifene) have become salient to breast cancer prevention. With the advent of these drugs, referred to as “chemoprevention,” a mandate has emerged to classify certain women as high risk for breast cancer to determine a group of legitimate users of the drugs. This article examines the development and standardization of the model used to create such a group of high-risk women. The author argues that while the model remains uncertain and controversial, it has become the standard tool for the many jobs associated with legitimizing chemoprevention use in the United States. It has become the assumed standard—shaping practices, identities, and definitions—through its organizational embeddedness in the multiple practices and public images of chemoprevention despite its uncertainty and widespread critique.
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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 | 0.135 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".