Incidence of lymphedema related to various cancers
Bibliographic record
Abstract
Cancer-related lymphedema (CRL) lacks internationally accepted definition and diagnostic criteria. The accurate incidence of CRL is therefore a challenge and the condition is likely underreported. Patients treated for cancer can develop CRL as a result of surgery, chemotherapy, and/or radiotherapy, which can lead to considerable psychosocial and physical morbidity, and decreased quality of life. Determining CRL incidence is crucial to inform care access and resource allocation, to best support patients affected by this lifelong condition. This review aimed to provide the latest CRL incidence estimates. Using four core databases (MEDLINE, Embase, Web of Science Core Collection, Cochrane Library), a literature search was performed to capture publications dated between 2015 and 2023. A total of 48 articles (33 prospective studies, 15 systematic reviews) met inclusion criteria, providing a sample size of 234,079 cancer patients. Findings revealed CRL incidence across cancer types varied, reported 2-74% in breast, 8-45% in gynecological and urological, 71-90% in head and neck and 2-29% in melanoma cancers. CRL incidence varied between 3 and 21% in preventative lymphedema surgery patients. Projected increases in cancer incidence and improved survival rates are expected to further escalate CRL incidence. Healthcare systems and professionals alike must therefore prepare to meet the growing needs of CRL patients.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".