Aromatase inhibitors and the incidence of Parkinson disease: A population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: Current guidelines recommend the treatment of hormone receptor-positive breast cancer with aromatase inhibitors (AIs) and tamoxifen in the adjuvant setting. Some observational studies have raised concerns that tamoxifen may be associated with an increased risk of Parkinson disease (PD). However, no studies have directly compared the risk of PD between AIs and tamoxifen in women diagnosed with breast cancer. METHODS: Using the UK Clinical Practice Research Datalink, the authors assembled a cohort of women newly diagnosed with breast cancer and newly treated with either AIs or tamoxifen between January 1, 1995, and December 31, 2017. Patients were followed 1 year after treatment initiation (ie, a 1-year lag) until an incident diagnosis of PD or were censored at death from any cause, the date of transfer out of the practice, or the end of the study period (December 31, 2018). Cox proportional hazards models with inverse probability of treatment weights were used to estimate weighted hazard ratios (HRs) and 95% confidence intervals (CIs) for PD comparing AIs with tamoxifen and accounting for more than 30 confounders. RESULTS: In all, 30,140 women with nonmetastatic breast cancer were identified: 13,838 initiated AIs, and 16,302 initiated tamoxifen. Compared with tamoxifen, AIs were not associated with an increased risk of PD (HR, 0.94; 95% CI, 0.60-1.47). Consistent results were observed across all secondary and sensitivity analyses. CONCLUSIONS: In this large observational study, the use of AIs, in comparison with tamoxifen, was not associated with an increased risk of PD in women diagnosed with nonmetastatic breast cancer in a real-world setting. LAY SUMMARY: Previous studies have indicated that tamoxifen may increase the risk of Parkinson disease in the treatment of breast cancer. However, no studies have directly compared the risk of Parkinson disease between aromatase inhibitors and tamoxifen. This study included 30,140 women diagnosed with breast cancer and treated with aromatase inhibitors or tamoxifen. Overall, compared with tamoxifen, aromatase inhibitors were not associated with an increased risk of Parkinson disease in women diagnosed with breast cancer. This study provides an important addition to the comparative safety profile of aromatase inhibitors and tamoxifen in the treatment of breast cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".