The effect of compression therapies and therapeutic modalities on lymphedema secondary to cancer: a rapid review and evidence map
Bibliographic record
Abstract
The identification of effective therapeutic modalities to manage lymphedema secondary to cancer is a high priority among patients and clinicians. Complex decongestive therapy (CDT) remains a fundamental intervention for individuals with lymphedema; however, interventions involving modalities such as low level laser therapy, specially designed compression systems, and compression pumps may be helpful to improve outcomes and reduce costs of care. We conducted a rapid review of the literature examining compression therapies and therapeutic modalities in the treatment of lymphedema secondary to cancer. A search of the electronic databases from June 2018 to October 2023 was performed including MEDLINE, EMBASE, and CINAHL. The electronic search yielded 438 potentially relevant citations with 40 randomized controlled trials included in the review, and 30 in the mapping process. Ninety-three percent (n = 37) of the trials included participants with a diagnosis of breast cancer. Across all categories and domains, all but two trials were rated as having 'some concerns' or a 'high risk of bias'. Intervention effects ranged from clinically insignificant to large effects on lymphedema volume. Evidence mapping suggests potential for benefit from (1) compression garments for the prevention of lymphedema, (2) interventions added to CDT in the intensive reduction phase, and (3) nighttime compression and compression pump treatments in the maintenance phase. A multi-centre collaborative research approach is needed to support the conduct of high-quality large-scale trials to inform the optimal type, timing, and combination of compression therapies and therapeutic modalities in the treatment of lymphedema secondary to cancer.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".