Prospective Surveillance and Risk Reduction of Cancer Treatment–Related Lymphedema: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PROBLEM IDENTIFICATION: Secondary lymphedema is a chronic condition that may result from cancer-related treatments. Evidence is emerging on prospective surveillance and risk reduction. LITERATURE SEARCH: Databases were systematically searched through April 1, 2019, for comparative studies evaluating interventions aiming to prevent lymphedema in patients with cancer. DATA EVALUATION: A random-effects model was used to perform meta-analysis, when appropriate. SYNTHESIS: A total of 26 studies (4,095 patients) were included, with 23 providing data sufficient for meta-analysis. Surveillance programs increased the likelihood of detecting lymphedema. Physiotherapy, exercise programs, and delayed exercise reduced the incidence of lymphedema. IMPLICATIONS FOR RESEARCH: Future research should standardize (a) evidence-based interventions to reduce the development of lymphedema and increase the likelihood of early detection and (b) outcome measures to build a body of evidence that leads to practice change. SUPPLEMENTAL MATERIAL CAN BE FOUND AT HTTPS: //onf.ons.org/supplementary-material-systematic-review-cancer-treatment-related-lymphedema.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".