Radiotherapy of the lymphatic drainage pathways in breast carcinoma: time for a reassessment
Bibliographic record
Abstract
Abstract Background The treatment administered for regional lymph nodes has altered dramatically in recent years. The state of the evidence on radiotherapy has also substantially improved and a reassessment is required. Materials and results Randomized studies are available on axillary surgery versus radiotherapy (AMAROS); on refraining from axillary dissection after a positive sentinel node (SN) when radiotherapy is planned (ACOSOG Z0011); and on adjuvant radiotherapy of the regional lymph nodes (LNs). ACOSOG Z0011 shows that refraining from axillary dissection is possible even when there are one or two positive SNs. The meta-analysis of the studies on adjuvant radiotherapy for the regional LNs (EORTC, Canadian, and French studies) shows a significant survival benefit with radiotherapy, and a Danish cohort study has confirmed this result. The reduction in breast cancer–specific mortality in these studies was partly based on a “systemic” effect of regional radiotherapy, with a reduction in the rate of distant metastases. Conclusions The principle applying in surgical treatment of the axilla is “less is not worse.” By contrast, the principle applying in radiotherapy of the regional lymph nodes is “more may be better in some circumstances.” There is as yet no clear explanation for the discrepancy between these two findings. An immunological effect (through immunogenic cell death due to irradiation) has been discussed. Further research is needed on which patients are today capable of benefiting from regional radiotherapy.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".