Neoadjuvant endocrine treatment for breast cancer: from bedside to bench and back again?
Bibliographic record
Abstract
In recent years, considerable attention has been paid to the role of neoadjuvant chemotherapy as a pluripotential test bed for the treatment of breast cancer. Although traditionally reserved to render inoperable disease operable, neoadjuvant chemotherapy is increasingly being used to improve the chance for breast-conserving surgery, to gain information on pathologic response rates for a more rapid assessment of new chemotherapy-biologic regimens, and also to study in vivo tumour sensitivity or resistance to the agent being used. Similarly, use of neoadjuvant endocrine treatment was also traditionally restricted to elderly or frail patients who were felt to be unsuitable for chemotherapy. It is therefore not surprising that, given the increasing realization of the pivotal role of endocrine therapy in patient care, there is enhanced interest in neoadjuvant endocrine therapy not only as a less-toxic alternative to chemotherapy, but also to assess tumour sensitivity or resistance to endocrine agents. The availability of newer endocrine manipulations and increasing evidence that the benefits of chemotherapy are frequently marginal in many hormone-positive patients is making endocrine therapy increasingly important in the clinical setting. The hope is that, one day, instead of preoperative endocrine therapy being restricted to the infirm and the elderly, it will be used in the time between biopsy diagnosis and surgery to predict which patients will or will not benefit from chemotherapy in the adjuvant setting.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".