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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".