Neoadjuvant endocrine therapy and window of opportunity trials: new standards in the treatment of breast cancer?
Bibliographic record
Abstract
Until recently, the use of neoadjuvant endocrine therapy was mainly restricted to those patients whose general frailty or comorbidities were contraindications to surgery. There is now increased evidence that certain patient populations (i.e. older patients with hormone-receptor positive disease) can gain as good a pathologic response, with considerably less toxicity, from neoadjuvant endocrine therapy than from neoadjuvant chemotherapy. Optimization of neoadjuvant endocrine therapy is therefore an important therapeutic goal. However, possibly of greater importance in the overall management of breast cancer, is the increased interest in exploring the effects of brief periods of endocrine therapy on in vivo biomarkers, in so called window of opportunity trials. These trials can not only be used to identify the mechanisms of action of novel agents but also to predict optimal subsequent adjuvant therapy for individual patients. While this paper will briefly review the history of neoadjuvant endocrine therapy, more emphasis will be on the evaluation of pivotal window of opportunity trials that will likely lead to a long awaited paradigm shift in the management of breast cancer.
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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 | 0.327 | 0.380 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| 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".