Chemoprevention Uptake among Women with Atypical Hyperplasia and Lobular and Ductal Carcinoma <i>In Situ</i>
Bibliographic record
Abstract
Abstract Women with atypical hyperplasia and lobular or ductal carcinoma in situ (LCIS/DCIS) are at increased risk of developing invasive breast cancer. Chemoprevention with selective estrogen receptor modulators or aromatase inhibitors can reduce breast cancer risk; however, uptake is estimated to be less than 15% in these populations. We sought to determine which factors are associated with chemoprevention uptake in a population of women with atypical hyperplasia, LCIS, and DCIS. Women diagnosed with atypical hyperplasia/LCIS/DCIS between 2007 and 2015 without a history of invasive breast cancer were identified (N = 1,719). A subset of women (n = 73) completed questionnaires on breast cancer and chemoprevention knowledge, risk perception, and behavioral intentions. Descriptive statistics were generated and univariate and multivariable log-binomial regression were used to estimate the association between sociodemographic and clinical factors and chemoprevention uptake. In our sample, 29.3% had atypical hyperplasia, 23.3% had LCIS, and 47.4% had DCIS; 29.4% used chemoprevention. Compared with women with atypical hyperplasia, LCIS [RR, 1.43; 95% confidence interval (CI), 1.16–1.76] and DCIS (RR, 1.54; 95% CI, 1.28–1.86) were significantly associated with chemoprevention uptake, as was medical oncology referral (RR, 5.79; 95% CI, 4.80–6.98). Younger women were less likely to take chemoprevention (RR, 0.61; 95% CI, 0.42–0.87), and there was a trend toward increased uptake in Hispanic compared with non-Hispanic white women. The survey data revealed a strong interest in learning about chemoprevention, but there were misperceptions in personal breast cancer risk and side effects of chemoprevention. Improving communication about breast cancer risk and chemoprevention may allow clinicians to facilitate informed decision-making about preventative therapy. Cancer Prev Res; 10(8); 434–41. ©2017 AACR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 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".