Immediate Lymphatic Reconstruction during Axillary Node Dissection for Breast Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
The objective of this study is to summarize the current body of evidence detailing the impact of immediate lymphatic reconstruction (ILR) on the incidence of breast cancer-related lymphedema (BCRL) following axillary node dissection (ALND). Methods: Medline and Embase databases were queried for publications, where ILR was performed at the time of ALND for breast cancer. Exclusion criteria included lymphaticovenous anastomosis for established BCRL, animal studies, non-breast cancer patient population studies, and descriptive studies detailing surgical technique. Meta-analysis was performed with a forest plot generated using a Mantel -Haenszel statistical method, with a random-effect analysis model. Effect measure was reported as risk ratios with associated 95% confidence intervals. The risk of bias within studies was assessed by the Cochrane Collaboration tool. Results: This systematic review yielded data from 11 studies and 417 breast cancer patients who underwent ILR surgery at the time of ALND. There were 24 of 417 (5.7%) patients who developed BCRL following ILR. Meta-analysis revealed that in the ILR group, 6 of 90 patients (6.7%) developed lymphedema, whereas in the control group, 17 of 50 patients (34%) developed lymphedema. Patients in the ILR group had a risk ratio of 0.22 (CI, 0.09 -0.52) of lymphedema with a number needed to treat of four. Conclusions: There is a clear signal indicating the benefit of ILR in preventing BCRL. Randomized control trials are underway to validate these findings. ILR may prove to be a beneficial intervention for improving the quality of life of breast cancer survivors.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".