Breast cancer prevention strategies in lobular carcinoma in situ: A decision analysis
Bibliographic record
Abstract
BACKGROUND: Women diagnosed with lobular carcinoma in situ (LCIS) have a 3-fold to 10-fold increased risk of developing invasive breast cancer. The objective of this study was to evaluate the life expectancy (LE) and differences in survival offered by active surveillance, risk-reducing chemoprevention, and bilateral prophylactic mastectomy among women with LCIS. METHODS: A Markov simulation model was constructed to determine average LE and quality-adjusted LE (QALE) gains for hypothetical cohorts of women diagnosed with LCIS at various ages under alternative risk-reduction strategies. Probabilities for invasive breast cancer, breast cancer-specific mortality, other-cause mortality and the effectiveness of preventive strategies were derived from published studies and from the National Cancer Institute's Surveillance, Epidemiology, and End Results database. RESULTS: Assuming a breast cancer incidence from 1.02% to 1.37% per year under active surveillance, a woman aged 50 years diagnosed with LCIS would have a total LE of 32.78 years and would gain 0.13 years (1.6 months) in LE by adding chemoprevention and 0.25 years (3.0 months) in LE by adding bilateral prophylactic mastectomy. After quality adjustment, chemoprevention resulted in the greatest QALE for women ages 40 to 60 years at LCIS diagnosis, whereas surveillance remained the preferred strategy for optimizing QALE among women diagnosed at age 65 years and older. CONCLUSIONS: In this model, among women with a diagnosis of LCIS, breast cancer prevention strategies only modestly affected overall survival, whereas chemoprevention was modeled as the preferred management strategy for optimizing invasive disease-free survival while prolonging QALE form women younger than 65 years. Cancer 2017;123:2609-17. © 2017 American Cancer Society.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".