Breast cancer‐related lymphedema: A comprehensive analysis of risk factors
Bibliographic record
Abstract
BACKGROUND: Breast cancer-related lymphedema is a devastating condition that negatively affects the quality of life of breast cancer survivors. We sought to identify risk factors that predicted the timing and development of lymphedema. METHODS: Women with breast cancer that underwent sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND) at our institution between 2007 and 2022 were identified and sociodemographic and clinical information was extracted. We used logistic regression analysis to identify risk factors for lymphedema and performed cox-regression analysis to predict the timing of lymphedema presentation after surgery. RESULTS: We identified 1,223 patients, of which 161 (13.2%) developed lymphedema within 1.8 (mean, SD = 2.5) years postoperatively. Patients with SLNB had significantly lower odds for lymphedema development (vs. ALND, OR = 0.29 [0.14-0.57]). Patients between 40 and 49 years of age, and 50-59 (vs. <40 years, OR = 2.14 [1.00-4.60]; OR = 2.42, [1.13-5.16] respectively), African American patients (vs. Caucasian, OR = 1.86 [1.12-3.09]), patients with stage II, III, and IV disease (vs. stage 0, OR = 3.75 [1.36-10.33]; OR = 6.62 [2.14-20.51]; OR = 9.36 [2.94-29.81]), and patients with Medicaid (vs. private insurance, OR = 3.56 [1.73-7.28]) had higher rates of lymphedema. Cox-regression analysis showed that African American (HR = 1.71 [1.08-2.70]), higher BMI (HR = 1.03 [1.00-1.06]), higher stage (stage II, HR = 2.22 [1.05-7.09]; stage III, HR = 5.26 [1.86-14.88]; stage IV, HR = 6.13 [2.12-17.75]), and Medicaid patients (HR = 2.15 [1.12-3.80]) had higher hazards for lymphedema. Patients with SLNB had lower hazards for lymphedema (HR = 0.43 [0.87-2.11]). CONCLUSION: Lymphedema has identifiable risk factors that can reliably be used to predict the chances of lymphedema development and enable clinicians to educate patients better and formulate treatment plans accordingly. LEVEL OF EVIDENCE: III (Retrospective study).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".