Neoadjuvant Chemotherapy in Breast Cancer: Review of the Evidence and Conditions That Facilitated Its Use during the Global Pandemic
Bibliographic record
Abstract
Practice and behaviour change in healthcare is complex, and requires a set of critical steps that would be needed to implement and sustain the change. Neoadjuvant chemotherapy for breast cancer is traditionally used for locally advanced disease and is primarily advantageous for surgical downstaging purposes. However, it does also offer patients with certain biologic subtypes such as the triple negative or Her2 positive breast cancers the opportunity to improve survival, even in early stage disease. During the height of the pandemic, an opportunity and motivation for the increased use of neoadjuvant therapy in breast cancer was identified. This paper describes the conditions that have supported this practice change at the provider and institutional levels. We also include our own institutional algorithm based on tumor biology and extent of disease that have guided our decisions on breast cancer management during the pandemic. Our processes can be adapted by other institutions and breast oncology practices in accordance with local conditions and resources, during and beyond the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".