Management of the axilla in early breast cancer: is it time to change tack?
Bibliographic record
Abstract
The standard surgical treatment of the axilla in patients with early breast cancer is about to undergo a radical change. Although axillary dissection is an excellent procedure for both staging and local control, particularly in the clinically positive axilla, it has considerable morbidity and may understage a significant proportion of patients, because it will usually miss micrometastases that can occur in approximately 10% of 'node negative' patients. An increasing number of patients whose tumours are either non-invasive (ductal carcinoma in situ; DCIS), micro-invasive, tubular cancers or low-grade T1a tumours without lymphovascular invasion may be spared axillary surgery because the risk of axillary disease is 0-3%. Many studies, both prospective trials and large retrospective series, show that axillary radiotherapy alone provides similar local control rates to axillary dissection in patients with clinically negative axillas. Primary treatment of the axilla with radiotherapy alone, however, does not allow appropriate staging. Sentinel lymph node biopsy is being increasingly used in patients with breast cancer to provide this information. When a sentinel node is identified it is equal to or better than axillary dissection for staging the axilla and, if the node is positive, it will help select patients who should then proceed to further axillary surgery or axillary radiotherapy. Although sentinel lymph node biopsy is being rapidly adopted in many centres worldwide, the results of randomized controlled trials are needed before it can be recommended as the standard of care.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".