First-Line Treatment with a Cyclin-Dependent Kinase 4/6 Inhibitor Plus an Aromatase Inhibitor for Metastatic Breast Cancer in Alberta
Bibliographic record
Abstract
In this analysis, we describe population-based outcomes for first-line treatment with a cyclin-dependent kinase 4/6 inhibitor (CDK4/6i) combined with an aromatase inhibitor (AI). All patients who were prescribed CDK4/6i + AI from January 2016 through June 2019 were included. Patient demographics, tumour and treatment characteristics were collected and described. Survival distributions were estimated using the Kaplan–Meier method. Multivariate analysis (MVA) was constructed to examine associations between potentially prognostic clinical variables and progression-free survival (PFS). In total, 316 patients were included. The median age was 61 years. After a median follow-up of 28.1 months, the median PFS was 37.9 months (95% CI, 26.7–NR). In the MVA, PR-negative tumour (HR, 2.37; 95% CI, 1.45–3.88; p = 0.001) and CDK4/6i dose reduction (HR, 1.51; 95% CI, 1.06–2.16; p = 0.022) predicted worse PFS. Median overall survival (OS) was not reached. The 30-month and 36-month OS rates were 74% and 68%, respectively. Of patients who progressed, 89% received second-line treatment. Median time to progression on second-line chemotherapy was 9.0 (5.8–17.6) months, and median time to progression on second-line hormonal therapy +/− targeted agent was 4.0 (3.4–8.6) months (p = 0.012). CDK4/6i + AI as first-line treatment for HR-positive, HER2-negative MBC in Alberta is justified based on favourable PFS and early OS outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".