TIME FROM SURGERY TO RT START IMPACTS BREAST CANCER SPECIFIC SURVIVAL IN INVASIVE BREAST CANCER PATIENTS
Bibliographic record
Abstract
There are significant pressures on the health care system, including initiating radiotherapy (RT) in a timely fashion. The aim of the project is to determine for invasive breast cancer (IBC) and ductal carcinoma in situ (DCIS) patients, in the modern era of subtyping and directed therapies, the maximal time intervals between surgery and RT start that will not impact the primary endpoint of locoregional recurrence (LRR) free survival and secondary endpoints of, breast cancer specific survival (BCSS), and overall survival (OS). Patients with IBC, not receiving chemotherapy, and patients with DCIS were identified from a provincial, prospectively collected database who were diagnosed between January 1, 2005 and December 31, 2016. Descriptive statistics were used to evaluate patient, tumour, and treatment characteristics. Univariate and multivariate Fine Gray substitution model were used to evaluate factors impacting on LRR, DDFS and BCSS and Cox proportional models for OS. 9319 patients with IBC were included. Median age was 65 years, 70% were Stage 1, 90% were Luminal A or B. Median time to RT start was 11 weeks from surgery. Time from surgery to RT start was not a significant predictor of LRR but was significant for BCSS (p<0.001) and OS (p<0.001). Radiotherapy start >16 weeks from surgery had a significant impact on BCSS (p ,0.001) and OS (p<0.001) but not on LRR. 2124 DCIS cases were included. Median age was 58 years, median time to RT start was 10 weeks from surgery. Time of RT start was not a significant predictor of LRR as a continuous variable or at >20 weeks from surgery. In the modern era, invasive breast cancer patients not receiving chemotherapy should have RT initiated withing 16 weeks from surgery to improve BCSS and OS. Impact on LRR did not seem to be impacted by time from surgery.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".