Women Planned for Immediate Lymphatic Reconstruction During Axillary Lymph Node Dissection Should Be Reconstructable: Improving Intraoperative Team Collaboration
Bibliographic record
Abstract
Introduction: Immediate lymphatic reconstruction (ILR) during axillary lymph node dissection (ALND) has been shown to reduce breast cancer-related lymphedema (BCRL). However, some authors report many “non-reconstructable” patients, meaning that there were no suitable lymphatics or veins available in the axilla to complete the reconstructive procedure once the extirpative portion was complete. In contrast, almost all of our patients planned for ALND/ILR have had appropriate donor and recipient vessels. The purpose of our study was to contrast the incidence of “non-reconstructable” patients in the literature with our experience and highlight tips to improve the reconstructable rate. Methods: Step 1: A systematic review identified publications on ILR during ALND, which reported the number of “non-reconstructable” patients. Step 2: A chart review of ILR cases at the University of Calgary was conducted. From both data sets, patient demographics, cancer stage, node dissection results, treatment details and operative details were collected. The data was then analyzed to identify factors that could contribute to the number of “non-reconstructable” patients. Results: 11 studies were identified in the review, which included 949 patients planned for ILR during ALND. One hundred and thirty-three (14%) were deemed “non-reconstructable,” and did not undergo ILR. Analysis of 68 consecutive ALND/ILR cases at the University of Calgary identified 4 (5.9%, p = . 03) “non-reconstructable” patients. A similar method of lymphatic mapping was used in the review studies as at the University of Calgary. The patients’ demographics and treatment details were similar in the review and our prospective series: average age (49 vs 54, p = . 07, BMI (27 vs. 27, p = . 47) and receipt of radiation (76.5% vs. 69%, p = . 79). The only difference noted was the presence and the level of involvement of a plastic surgeon throughout the extirpative portion of the procedure, in order to identify and preserve vessels. At our institution, the plastic surgeon attends throughout and participates in a well-coordinated “dance” between the oncologic surgeon and the plastic surgeon. This coordination was not described in any of the studies reviewed. Conclusions: For ILR, coordinated plastic surgical involvement during ALND may reduce the number of “non-reconstructable” patients.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".