A systematic review and meta-analysis of the incidence of breast cancer-related lymphoedema due to treatment combinations
Bibliographic record
Abstract
Abstract BACKGROUND Breast cancer related-lymphoedema (BCRL) is a chronic, debilitating disease for which there is no cure. A meta-analysis was conducted to estimate the association between different treatment combinations on the incidence of BCRL. METHODS The review was conducted according to PRISMA guidelines with four databases searched for studies published from 2000-2020, including OVID Medline, OVID Embase, Cochrane Library for Registered Controlled Trials, and Cumulative Index to Nursing and Allied Health, yielding 2640 studies. A random effects model was used to determine BCRL incidence rates stratified by treatment types of the qualifying studies. RESULTS The pooled incidence rate was 23% (95% CI 20.8 – 25.4) for patients who received axillary lymph node dissection (ALND) and 5.6% (95% CI 4.5 – 6.8) for patients who underwent sentinel lymph node biopsy (SLNB). A higher level of intervention to the axilla was identified as the key factor associated with significantly increased BCRL incidence, including ALND (p<0.001), the number of lymph nodes removed (p<0.001), and axillary radiotherapy (p<0.001). Higher patient BMI was also identified to increase BCRL incidence. Combinations of other treatments, in conjunction with ALND or SLNB, did not lead to statistically significant differences in incidence. Furthermore, different diagnostic criteria resulted in substantial variation in BCRL incidence rates. CONCLUSION Axilla intervention was associated with increased BCRL incidence including ALND, number of lymph nodes removed, and radiotherapy. The inclusion of additional surgical or non-surgical treatments did not. This analysis re-emphasises the clear need for standardised reporting of patient treatments as well as universally applied diagnostic protocols.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.048 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".