Risk Factors Associated with Breast Cancer-Related Lymphedema: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Lymphedema is a chronic, progressive condition that commonly occurs after treatment for breast cancer. Therefore, this study aimed to assess the incidence and risk factors of breast cancer-related lymphedema (BCRL). Methods: PubMed, Web of Science, Embase, MEDLINE, CNKl, Wang Fang DATA, Vip Database, and SinoMed were searched from January 2000 to January 2022. Risk of bias was assessed using the Newcastle-Ottawa Scale. Estimates of pooled incidence and risk factors estimates were calculated with 95% confidence intervals (CI), with sub-group analyses according to country, study design, population characteristics, the definition of lymphedema, and risk of bias. Heterogeneity was measured using I2 and publication bias was analyzed by funnel plot. Results: 34 studies comprising 23,988 participants were included in this study, with a follow-up period ranging from 1 to 10.2 years. The estimated pooled cumulative incidence at 1,2,3,5 years post-operative for patients respectively was 20%, 17%, 18% and 23%. Factors like: stage III cancer (RR: 1.34; 95% Cl: 1.17-1.52), age≥50 (RR: 1.47; 95% Cl: 1.23-1.76), BMI ≥25 (RR: 2.09; 95% Cl: 1.85-2.36), ALND (RR: 2.72; 95% Cl: 1.89-3.92), axillary radiotherapy (RR: 2.19; 95% Cl: 1.64-2.92), Neo-adjuvant chemotherapy (RR: 1.61; 95% Cl: 1.08-2.39), adjuvant taxane-based chemotherapy (RR: 1.65; 95% Cl: 1.25-2.19) and postoperative wound complications (RR: 1.66; 95% Cl: 1.13- 2.43) were significantly associated with BCRL. Conclusions: Our analyses suggest that BCRL risk is significantly associated with cancer stage, age, BMI, ALND, radiotherapy, chemotherapy, and postoperative wound complications.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".