Neoadjuvant Systemic Therapy in Breast Cancer: Use and Trends in Radiotherapy Practice
Bibliographic record
Abstract
BACKGROUND: The use of neoadjuvant systemic therapy (nast) in the treatment of breast cancer is increasing, and the role of adjuvant radiation therapy (rt) in that setting is uncertain. We sought to review and report the use of nast, its trends over time, and its relationship with the prescribing patterns of locoregional rt in a provincial cancer system. METHODS: Patients with stages i-iii breast cancer diagnosed during 2007-2012 were identified using a provincial database. Patient, tumour, and treatment characteristics were extracted. Multivariable logistic regression analyses were used to assess associations with the use of nast. Kaplan-Meier and Cox regression were used for survival analyses. RESULTS: Of the 11,658 patients who met the inclusion criteria, 602 (5%) had received nast. Use of nast was more frequent in stage iii patients (53%) than in stages i and ii patients (2%). In clinically lymph-node positive patients, a pathology assessment was made approximately 50% of the time. Higher clinical tumour stage and increasing clinical nodal stage predicted for increasing use of nast and of nodal rt after nast, but pathologic nodal status after nast was not associated with use of nodal rt. A statistically significant survival difference was observed between patients in the nast and no-nast groups, but that significance disappeared in a multivariable Cox regression analysis. CONCLUSIONS: This population-based study demonstrated 5% use of nast for breast cancer. Most patients received nodal rt after nast, and nodal rt was not associated with pathologic stage after nast. Findings likely reflect the realities of clinical practice and show that reliance on clinical nodal staging results in outcomes similar to those reported in the literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".