Impact of internal mammary node inclusion in the radiation treatment volume on the outcomes of patients with breast cancer treated with locoregional radiation after six years of follow-up.
Bibliographic record
Abstract
81 Background: There is ongoing controversy about radiotherapy (RT) to internal mammary nodes (IMNs). Proponents of IMN RT cite the survival benefit seen in postmastectomy RT trials that included IMNs. However, others point out that benefit cannot be definitively attributed to IMN inclusion, as other lymph node regions were included in the RT arms. The issue is important, as IMN RT potentially increases cardiac and respiratory morbidity. Methods: 2,413 women referred to a provincial RT program with newly diagnosed node positive, or T3/4N0 non-M1 invasive breast cancer, treated with a complete course of locoregional RT from 2001 to 2006, were retrospectively identified in a provincial database. IMN RT inclusion versus exclusion was determined through review of patient charts and RT treatment plans. Breast cancer-specific survival (BCSS), relapse-free survival (RFS), and overall survival (OS) were compared between the two groups using univariate and multivariable analyses. Results: Analyses were performed at a median follow-up of 6.2 years. 41.4% of the subjects received IMN RT. The 5-year BCSS for the IMN inclusion and exclusion group was 84.8% versus 82.9%, respectively (HR 0.93 [95% CI 0.76, 1.14]; p=.51); the 5-year RFS was 87.4% versus 86.9% (HR 0.993 [0.83, 1.19]; p=0.94); and the 5-year OS was 84.8% versus 82.9% (HR 0.84 [0.70, 1.01]; p=0.06). After controlling for potentially confounding variables, there was no significant difference in BCSS (HR 0.96 [0.78, 1.18], p=0.88), RFS (HR 1.02 [0.84, 1.22], p=0.87), or OS (HR 0.91 [0.76, 1.10]; p=0.35). Conclusions: After a median follow-up of 6.2 years, this population-based study shows no benefit from including IMNs in the locoregional RT volume after adjusting for other prognostic and treatment variables.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".