Risk factors for lower extremity lymphedema following treatment of gynecologic cancers: a meta-analysis
Bibliographic record
Abstract
Objective To investigate the multiple risk factors for lower extremity lymphedema in patients following treatment of common gynecologic cancers by meta-analysis for systematic analysis and comprehensive quantitative study. Methods Clinical trials published up until August 2016 were retrieved from PubMed, Embase, and the Cochrane Library. The quality of the included studies was assessed by the Newcastle-Ottawa Scale, and data analysis was performed using Stata 14.0 and RevMan 5.3. The strength of the associations between risk factors and gynecologic cancer-related lower extremity lymphedema was described as odds ratio (OR) and 95% confidence intervals (CI). Results Eighteen studies were included in the meta-analysis, and 8 relevant factors were identified. The risk factors for lower extremity lymphedema after treatment of gynecologic cancer mainly included radiotherapy (OR=2.45, 95%CI: 2.05-2.95, P=0.000), FIGO stage (OR=2.29, 95%CI: 1.66-3.14, P=0.000), and pelvic lymph node dissection (OR=2.00, 95%CI: 1.02-3.91, P=0.040). Conclusions Radiotherapy, FIGO stage, and pelvic lymph node dissection are the main risk factors for lower extremity lymphedema after treatment of gynecologic cancers. Key words: Gynaecological neoplasms; Lower extremity lymphedema; Risk factors
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.003 | 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".