What Factors Influence Decision-Making about Breast Cancer Chemoprevention among High-Risk Women?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Estrogen exposure is one of the strongest risk factors for breast cancer development. Chemoprevention with selective estrogen receptor modulators (SERM), such as tamoxifen and raloxifene, has been shown in randomized controlled trials to reduce breast cancer incidence by up to 50% among high-risk women. Despite the strength of this evidence, there is significant underutilization of chemoprevention. Given the relatively few modifiable breast cancer risk factors, SERM use provides an important strategy for the primary prevention of this disease. Understanding factors which influence chemoprevention decision-making will inform efforts to implement breast cancer risk assessment and increase chemoprevention uptake in clinical practice. Cancer Prev Res; 10(11); 609–11. ©2017 AACR. See related article by Holmberg et al., p. 625
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it