Targeted Axillary Dissection in Node-Positive Breast Cancer: A Retrospective Study and Cost Analysis
Bibliographic record
Abstract
Introduction Targeted axillary dissection (TAD) is a novel technique in the field of surgical oncology. During TAD, patients with node-positive breast cancer who clinically responded to neoadjuvant chemotherapy undergo resection of a previously proven metastatic node together with sentinel lymph node dissection (SLND). We aimed to assess the success rates of seed insertion and seed retrieval in the Canadian setting, as well as hospital costs of the procedure. Methods Patients converted to clinically node-negative status post-neoadjuvant chemotherapy underwent TAD. Before surgery, an iodine-125 radioactive seed was inserted in the previously proven metastatic node. The seed node was resected together with an SLND. Axillary lymph node dissection (ALND) was performed in all patients with residual metastases. Results Radioactive seeds were successfully inserted in 34/35 patients. In 34 patients, the targeted node was successfully resected with the radioactive probe during TAD. In one patient, the seed was retrieved inferiorly in the axilla during surgery. There was no adverse event. In total, 50% (17/34) of patients had no residual metastases and were able to avoid ALND. Eight out of 17 patients who underwent ALND did not have any residual disease in their specimen. The mean cost of TAD was 25% superior to the mean cost of ALND (p = 0.02). However, the mean total cost of the hospital stay for TAD was 20% superior to the mean cost of ALND (p = 0.11). The mean cost of TAD was 4,322 Can$ (Canadian dollars), similar to the mean cost of both ALND and SLND performed during the same procedure (4,479 Can$). Conclusions TAD was successful in 97% of patients. Despite increased procedural costs, with a lesser impact on total hospital stay costs, TAD was beneficial in 50% of patients. These patients avoided the unnecessary morbidity associated with ALND.
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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".