Aromatase inhibitors and the risk of colorectal cancer in postmenopausal women with breast cancer
Bibliographic record
Abstract
Background: A large trial of postmenopausal women with breast cancer reported an imbalance in colorectal cancer events with aromatase inhibitors (AIs), compared with tamoxifen in the adjuvant setting. This unexpected signal was observed within 3 years of randomization. To date, no observational studies have examined this important safety question in the natural setting of clinical practice. Thus, the objective of this study was to determine whether AIs, when compared with tamoxifen, are associated with increased risk of colorectal cancer in postmenopausal women with breast cancer. Patients and methods: Using the UK Clinical Practice Research Datalink, we identified women, at least 55 years of age, with breast cancer newly treated with either AIs or tamoxifen between 1 January 1996 and 30 September 2015, with follow-up until 30 September 2016. High-dimensional propensity score-adjusted Cox proportional hazards models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) of incident colorectal cancer associated with AIs when compared with tamoxifen overall, by cumulative duration of use, and time since initiation. All exposures were lagged by 1 year for latency considerations. Results: A total of 9701 and 8893 patients initiated AIs and tamoxifen as first-line hormonal therapy (median follow-up of 2.4 and 2.9 years, respectively). Compared with tamoxifen, AIs were not associated with an increased risk of colorectal cancer (incidence rates of 150 per 100 000 person-years in both groups; adjusted HR: 0.90, 95% CI: 0.53-1.52). Similarly, there was no evidence of an association with cumulative duration of use (P-heterogeneity = 0.54), and time since initiation (P-heterogeneity = 0.66). Conclusions: In this first population-based study, the use of AIs was not associated with an increased risk of colorectal cancer. These findings should provide reassurance to the concerned stakeholders.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".