Factors that determine whether a patient receives completion axillary lymph node dissection after a positive sentinel lymph node biopsy in breast cancer in British Columbia
Bibliographic record
Abstract
BACKGROUND: Completion axillary lymph node dissection (CALND) is recommended in the setting of positive sentinel lymph node biopsy (SLNB) but is associated with a higher rate of postoperative complications. In this study, the characteristics and outcomes of patients who did and did not have CALND are compared. METHODS: We identified all patients with breast cancer with positive sentinel lymph nodes (SLNs) who did not have concurrent CALND from 2003 to 2006 using a prospectively collected database (British Columbia Cancer Breast Outcomes database) and retrospective chart review. Patient and tumour characteristics were compared between those who received CALND and those who did not. RESULTS: Among 185 patients with positive SLNs identified by SLNB, 90 had a CALND and 95 had no further surgical therapy. Patients who did not receive CALND had more sentinel nodes removed (p < 0.001), a lower percentage of positive SLNs (p < 0.001) and lower pathologic N stage (p = 0.044) than those who did receive CALND. The size of the breast lesion, size of the largest SLN deposit, estrogen receptor status, grade, lymphovascular invasion, histology and multifocality were not significantly different between groups. Sixty-two percent of women who did not have CALND received radiation to the axilla. Postoperative complication rates (including lymphedema) were higher in the CALND group (21%) compared with the SLNB group (7%). The rates of locoregional recurrence (1% in both groups) and systemic metastases (6% in the CALND group v. 8% in the SLNB group) were similar at 36 months' follow-up. CONCLUSION: Compared with women who had CALND, women who did not receive CALND had on average a lower N stage with 3 or more SLNs removed and less than 50% node positivity. Most of these women received radiation therapy to the axilla and had comparable recurrence rates to those who had CALND.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".