Breast Cancer-Related Lymphedema: A Common Challenge among Nigerian Breast Cancer Survivors
Bibliographic record
Abstract
Background: Lymphedema is one of the most prevalent yet under-recognized complication of breast cancer treatment, with its prevalence largely unexplored in Nigeria and across much of sub-Saharan Africa. Methods: A cross-sectional study was conducted among breast cancer survivors at least 6 months post-mastectomy and axillary lymph node dissection. Lymphedema was diagnosed using multiple methods: patient-reported arm swelling, arm measurements (≥2 cm difference compared to the contralateral arm), a >10% difference in extracellular water (ECW) using bioimpedance analysis, and a lower threshold of 5% to capture subclinical lymphedema. Using patient report as the gold standard, the accuracy of the various diagnostic methods was assessed. The relationship between clinical variables and lymphedema was tested using univariate logistic regression analysis. Results: Fifty-one patients with a median age of 51 years and a median duration of 40 months post-surgery (10–62 months) were evaluated. The prevalence of lymphedema was 39.2% based on symptoms, 33% using arm measurements, 22.2% using bioimpedance analysis at a threshold of >10% difference in ECW, and 46.7% at a threshold of 5%. An ECW difference of >5% had the highest sensitivity (65%), while an ECW difference at 10% threshold had the best specificity (89%). Obesity was the only clinical variable associated with lymphedema in this cohort (p = 0.018). Conclusion: Breast cancer-related lymphedema (BCRL) appears common among Nigerian breast cancer patients. Its occurrence should be preempted, particularly in obese patients in whom preventive measures may be instituted. These findings highlight the potential value of incorporating BCRL awareness and management into breast cancer care in Nigeria.
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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 | 0.001 |
| 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) | 0.001 | 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 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".