Evaluating the Feasibility of Performing Window of Opportunity Trials in Breast Cancer
Bibliographic record
Abstract
BACKGROUND: The waiting period to surgery represents a valuable "window of opportunity" to evaluate novel therapeutic strategies. Interventional studies performed during this period require significant multidisciplinary collaboration to overcome logistical hurdles. We undertook a one-year prospective window of opportunity study to assess feasibility. METHODS: Eligible newly diagnosed postmenopausal, estrogen receptor positive breast cancer patients awaiting primary surgery received anastrozole daily until surgery. Feasibility was assessed by (a) the proportion of patients who consented and (b) completed the study. Comparison of pre- and poststudy Ki67 labelling index and cleaved caspase 3 scores (CC3) was performed. RESULTS: 22/131 (16.8%) patients were confirmed eligible and 20/22 (91%) patients completed the study. 19/20 (95%) patients agreed to undergo optional additional tissue biopsies. The mean duration of anastrozole use was 24.7 (15-44) days. There were a statistically significant decline in mean Ki67 indices of 48.8% (p < 0.001) and a trend towards significance in the decline of CC3 (p = 0.17) when comparing pre- with posttreatment values. CONCLUSION: window of opportunity trials in breast cancer are a feasible way of assessing the biologic efficacy of different therapies in the presurgical setting. The majority of eligible women were willing to participate including undergoing additional tissue biopsies.
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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 | 0.086 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".