Immediate Lymphatic Reconstruction May Decrease the Incidence of Lymphedema in Patients Undergoing Axillary Lymph Node Dissection
Bibliographic record
Abstract
Background: Approximately one-third of patients undergoing axillary lymph node dissection (ALND) for breast cancer will develop breast cancer–related lymphedema (BCRL). To prevent BCRL, immediate lymphatic reconstruction (ILR) has been proposed, whereby lymphatics cut during the ALND are anastomosed to adjacent veins to restore lymphatic drainage. As evidence for ILR grows, the aim of this study was to investigate its efficacy at our institution. Methods: This prospective single-center study included 17 women undergoing ALND with ILR. Our primary outcome was the incidence of BCRL, diagnosed using a greater than 10% relative difference in arm volume. Use of compression therapy was also followed. Our secondary outcome was patient-reported outcome measures, determined by the validated Lymphedema Quality of Life (LYMQOL-Arm) survey. Postoperatively, patients were followed up at regular intervals for a minimum of 18 months. Results: The median age of included patients was 49 (interquartile range [IQR] 46–58). The average follow-up time was 34.4 months (range 18–42 mo). Two patients met the criteria for BCRL. Patients with BCRL had a significantly higher median arm volume difference of 27.5% (IQR 21.8%–33.2%) versus 4.2% (IQR 1.6%–7%; P = 0.02). Three patients used compression to control symptoms. Patients without lymphedema scored better in several domains of the LYMQOL-Arm survey, including function, appearance, and overall quality of life; however, these results did not meet statistical significance. Conclusions: ILR in patients undergoing ALND is associated with a low incidence of BCRL. Our study is one of the first to use patient-reported outcome measures to study ILR.
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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 | 0.004 |
| 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) | 0.002 | 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".