Adherence to osteoporosis screening guidelines in seniors with breast cancer treated with anti-estrogen therapy: A population-based study.
Bibliographic record
Abstract
72 Background: Aromatase inhibitors (AI) improve recurrence and survival rates in women ≥ 65 with hormone-receptor positive (HR+) breast cancer (BC). However, AI’s are associated to increased bone demineralization in a population already at risk for osteoporosis. Bone mineral densitometry (BMD) is therefore recommended at treatment start, to minimize treatment-related toxicity. This study assesses compliance to BMD recommendations in older BC survivors treated with an AI and identifies predictors of non-adherence to guidelines. Methods: A population-based historical prospective cohort study was conducted in Quebec (Canada), where universal health insurance provides medical and pharmaceutical services. All women ≥ 65 with incident stage I, II or III BC (1998-2012) were identified from the provincial cancer registry. Administrative claims were accessed to track receipt of BMD (±12 months from treatment start) and potential predictors: age, economic status, Charlson comorbidity index, breast and axillary surgery, chemotherapy, radiotherapy, bisphosphonate use, adherence to anti-estrogen therapy (AET), physician specialty and having a primary care physician (PCP). Multivariate logistic regression was performed using generalized estimating equations to identify predictors of BMD, adjusting for clustering within physicians. Results: Of 16,480 patients, 36.1% had a BMD at AET start. This increased to 58.4% for women on AI. Factors predicting baseline BMD non-use in AI patients were: older age (OR 0.42; 95% CI, 0.36 – 0.49), lower annual income (OR 0.57; 95%CI, 0.47 – 0.70), not having a PCP (OR 0.77; 95%CI, 0.68 – 0.86), chemotherapy and radiotherapy outside of guidelines (OR 0.80; 95%CI, 0.66 – 0.96 and 0.69; 95%CI, 0.54 – 0.87, respectively), non-surgical specialist managing AET (OR 0.81; 95%CI, 0.67 – 0.98), older physicians (OR 0.61; 95% CI, 0.46 – 0.81), and non-adherence to AET (OR 0.69; 95%CI 0.60-0.79). The same predictors persisted in patients on any AET. Conclusions: Bone health management in seniors receiving AET is suboptimal. Factors related to underutilization of BMD can be easily identified and used to optimize chronic care of older BC survivors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".