Effect of treatment interruptions on overall survival in patients with triple-negative breast cancer
Bibliographic record
Abstract
INTRODUCTION: Currently, there are no data regarding the impact of treatment interruptions during radiotherapy for breast cancer. In this study, we examine the correlation between treatment interruptions during radiotherapy and outcomes in triple-negative breast cancer patients. METHODS: A total of 35 845 patients with triple-negative breast cancer treated between 2010 and 2014 were identified and analyzed from the National Cancer Database. The number of interrupted radiotherapy treatment days was calculated as the difference between the total elapsed days from the start to end of radiation treatment (both initial treatment and boost treatment, when boost was administered) and the total number of expected treatment days, defined as the number of expected treatment days with an addition of 2 weekend days for every multiple of 5 treatment days. Binomial multivariate regression analysis was used to detect correlates of treatment interruptions, and propensity-score matched multivariable Cox proportional hazard models were used to evaluate the association between treatment interruption and overall survival (OS). RESULTS: When modeled as a continuous variable, longer treatment duration was associated with poorer OS (hazard ratio [HR] = 1.023, 95% confidence interval [CI] = 1.015 to 1.031). In reference to 0-1 days of interruption, patients with 2-5 interrupted days (HR = 1.069, 95% CI = 1.002 to 1.140 interrupted days), 6-10 interrupted days (HR = 1.239, 95% CI = 1.140 to 1.348 interrupted days), and 11-15 interrupted days (HR = 1.265, 95% CI = 1.126 to 1.431 interrupted days) experienced increasing likelihood of mortality. CONCLUSION: In the first study of its kind, we report a correlation between treatment interruptions during adjuvant radiotherapy in triple-negative breast cancer and OS.
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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) | 0.000 | 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".