Vasomotor (VM) and musculoskeletal (MSK) symptoms and association with outcomes on extended adjuvant letrozole therapy: Analyses from NCIC CTG MA.17.
Bibliographic record
Abstract
524 Background: Surrogate markers of clinical outcomes can potentially guide cancer therapy. New onset MSK symptoms and VM symptoms on aromatase inhibitors (AI) for hormone receptor positive (HR+) early breast cancer (EBC) have been investigated as markers of clinical outcomes, but results have been conflicting. MA.17 showed improved disease free survival (DFS) with letrozole (L) as extended adjuvant therapy after 5 years of tamoxifen (T). We performed this exploratory analysis of new onset symptoms and their association with clinical outcomes in the MA.17 trial. Methods: Patients (pts) with HR+ EBC were randomized to receive L or placebo (P) for 5 years after 5 years of T. Symptoms were collected according to CTC v. 2.0 at baseline, 1 month (mo), 6 mos, and every 12 mos thereafter. Analyses included pts with new symptoms of any grade who received therapy and excluded pts with baseline symptoms, EBC not HR+, and deaths/ relapses prior to each time point. Symptoms were categorized as VM - hot flashes/flushes, flushing, or sweating; and MSK - arthritis, arthralgia, myalgia, bone pain, or MSK other. Multivariate Cox Models adjusting for age, nodal status, duration of T and prior chemotherapy were used for analysis of DFS, distant DFS (DDFS), and overall survival (OS) assessed before unblinding. Hazard Ratios [HR] and 95% confidence intervals (CI) for patients with either VM or MSK symptoms are presented below for L using pts with no symptoms as controls. Results: Emergent VM symptoms were associated with improved DFS and DDFS on L (Table). No association was found for MSK symptoms at any time point. No association between either MSK or VM and OS was found at any time point. Conclusions: New VM symptoms initiating with extended letrozole were associated with improved outcomes. Similar findings have been seen with up-front AI therapy. Our results should be confirmed as they are of potential significance in guiding therapy. [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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".