Prevalence and determinants of lymphedema in newly diagnosed Nigerian breast cancer patients using bioimpedance estimations
Bibliographic record
Abstract
Background: Breast cancer-related lymphedema (BCRL) is common and has significant impact on quality of life. Very little is known about BCRL in sub-Saharan Africa. Generally, BCRL has been mostly evaluated post treatment, with very limited data on the prevalence of pre-treatment BCRL at baseline. This study presents the prevalence and clinical associations of lymphedema among newly diagnosed, treatment-naive breast cancer patients in a Nigerian cohort using bioimpedance estimations. Methods: Consecutively consenting, newly diagnosed, treatment-naive breast cancer patients were assessed for upper limb lymphedema using bioimpedance measurements of the extracellular fluid and the single-frequency bioelectrical impedance analysis value at 5 kHz. Patients were classified as having lymphedema if there was >10% difference in arm measurements or if the ratios of the arm measurements were >3 SD above a normative mean generated from representative controls. Regression analysis was performed to determine clinical variables associated with lymphedema. Results: . The majority (70%) had stage III disease. All measurements were significantly higher in cases than controls. Using various definitions, the prevalence of lymphedema was between 11.7% and 14.3%. Various clinical variables relating to clinical stage were significantly associated with lymphedema. Conclusion: The predominance of locally advanced disease in the Nigerian setting is associated with high pre-treatment lymphedema rates. This may set the stage for higher rates in the post-operative setting. Management of lymphedema should be incorporated into the treatment planning.
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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 | 0.001 | 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".