A randomized trial (MA.17R) of extending adjuvant letrozole for 5 years after completing an initial 5 years of aromatase inhibitor therapy alone or preceded by tamoxifen in postmenopausal women with early-stage breast cancer.
Bibliographic record
Abstract
LBA1 Background: Five years of aromatase inhibitor (AI) therapy either as up-front treatment or after 2-5 years of tamoxifen has become the standard of care for postmenopausal women with hormone receptor positive early breast cancer. Extending treatment with an AI to 10 years may further reduce the risk of breast cancer recurrence. Methods: We conducted a double-blind, placebo-controlled trial (Canadian Cancer Trials Group MA.17R) to test the efficacy of extending AI treatment for an additional five years using letrozole. The primary endpoint was disease-free survival. Results: A total of 1,918 women with early stage breast cancer were enrolled (median follow-up 75 months, 6.3 years). A total of 165 disease-free survival (DFS) events (67 on letrozole and 98 on placebo) occurred, of which 42 versus 53 were distant recurrences on letrozole and placebo, respectively. There were 200 deaths (100 in each treatment group). The 5 year DFS was respectively 95% for patients receiving letrozole versus 91% for those on placebo (HR 0.66; P = 0.01) from a two-sided log-rank test stratified by nodal status, prior adjuvant chemotherapy, interval between last dose of AI therapy and randomization, and duration of prior tamoxifen at randomization. The 5 year overall survival was respectively 93% for subjects on letrozole and 94% on placebo with a HR of 0.97 (P = 0.83). The annual incidence rate of contralateral breast cancer was 0.21% for subjects on letrozole versus 0.49% on placebo (P = 0.007). Conclusions: Compared to 5 years of AI treatment as initial therapy or preceded by 2-5 years of tamoxifen, extending AI treatment to 10 years significantly improves disease-free survival. Further analyses will provide a comprehensive picture of toxicities and QOL. Clinical trial information: NCT00754845.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".