Should all postmenopausal patients with hormone receptor-positive breast cancer receive initial therapy with aromatase inhibitors?
Bibliographic record
Abstract
BACKGROUND: In the past few years aromatase inhibitors (AIs) have shown superior efficacy to the previous standard adjuvant endocrine therapy, tamoxifen, and are now recommended as part of current adjuvant endocrine therapy. A range of treatment strategies have been explored. MATERIALS AND METHODS: We assess the role of initial AI therapy for postmenopausal women with hormone receptor-positive breast cancer and consider the relative value of initial therapy with an AI compared with switch or extended (>5-yr) adjuvant therapy. RESULTS: Both initial AI therapy and switching/sequential tamoxifen followed by an AI are associated with longer disease- and relapse-free survival versus 5 years of tamoxifen alone. Trials comparing initial therapy with the sequence of tamoxifen followed by an AI have not demonstrated any major efficacy differences between the treatment strategies. Several analyses have been conducted to identify prognostic or predictive markers of treatment benefit to enable selection of the most appropriate adjuvant therapy. CONCLUSIONS: Initial and switching/sequential regimens are equally appropriate adjuvant treatment options for postmenopausal patients with hormone receptor-positive breast cancer. The exact tumour biology which allows for initial AI therapy has not yet been determined with certainty.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".