Vascular toxicities of endocrine therapy in early-stage breast cancer: Encouraging observations in a nontrial setting.
Bibliographic record
Abstract
68 Background: Endocrine therapy (ET) is the standard of care for postmenopausal (PM) women with early stage breast cancer (EBC). Studies suggest higher risk of vascular toxicities (VT) on aromatase inhibitor (AI) therapy. We report incidence/discontinuation rates of VTs in a cardiac clinic. Methods: PM women with hormone receptor positive EBC treated with ET (tamoxifen (T) ± AI) at Ottawa Hospital Cancer Center 01/99-2/06. Data included: demographics, vascular co-morbidities (VCM), ET, duration, VTs. Results: 626 pts, median age 59 years (r: 30-92), median follow-up 98 months (m), stage: I (196 pts), II (341 pts) III (89 pts) EBC. Majority (52.5%) pts had VCM at ET initiation; hypertension (HTN) (36%), hyperlipidemia (HYLP) (17%), coronary disease (12%), thrombosis (9%), angina (6%) TIA (6%). Treatment discontinued due to VT 3x more with T vs. AI. Most common VTs: edema, arrhythmias (ARR), cardiovascular (CVS) event, and HYLP. With Letrozole and T, previous VCM significantly increased risk of developing VT (chi-square: P=0.022 and 0.009). Time to develop VT shortest for T and exemestane. Previous VCM did not affect this interval. Longer exposure to T correlated with higher VT rate (t-test: p=0.046) not seen with AIs. Exposure to multiple AIs associated with higher VT rate (t-test: p=0.009). Conclusions: This cohort study reports similar VT rates with AI therapy as reported in the literature. T was associated with higher discontinuation rates (10.5%) due to VTs compared to AIs (2.8-3.3%). Longer duration of AI therapy was not associated with increased risk of VTs. These encouraging results reflect the real-life experience of women exposed to ET. [Table: see text]
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".