The impact on clinical outcomes of post-operative radiation therapy delay after neoadjuvant chemotherapy in patients with breast cancer: A multicentric international study
Bibliographic record
Abstract
INTRODUCTION: Radiation therapy (RT) is frequently used for post-operative treatment in breast cancer (BC) patients who received preoperative systemic therapy (PST) and surgery. Nevertheless, the optimal timing to start RT is unclear. MATERIAL AND METHODS: Data from BC patients who underwent chemotherapy as PST, breast surgery and RT at 3 Institutions in Brazil and Canada from 2008 to 2014 were evaluated. Patients were classified into three groups regarding to the time to initiation of RT after surgery: <8 weeks, 8-16 weeks and >16 weeks. RESULTS: A total of 1029 women were included, most of them (59.1%; N = 608) had clinical stage III. One hundred and forty-one patients initiated RT within 8 weeks, 663 between 8 and 16 weeks and 225 beyond 16 weeks from surgery. With a median follow-up of 32 months, no differences in disease-free survival (DFS), overall survival and locoregional recurrence-free survival (LRRFS) were observed of time to indicated RT (<8 weeks versus 8-16 weeks versus >16 weeks). However, in luminal subtype patients (46.5%; N = 478), initiation of RT up to 8 weeks after surgery was associated with better LRRFS (<8 weeks versus >16 weeks: HR 0.22; 95%CI 0.05-0.86; p = 0.03), with a tendency to a better DFS (<8 weeks versus >16 weeks: HR 0.50; 95%CI 0.25-1.00). CONCLUSION: RT initiated up to 8 weeks after surgery was related to better LRRFS in luminal BC patients who underwent PST. Our results suggest that early start of RT is important for these patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".