External Validation of a 5-Factor Risk Model for Breast Cancer–Related Lymphedema
Bibliographic record
Abstract
Importance: Secondary lymphedema is a common, harmful side effect of breast cancer treatment. Robust risk models that are externally validated are needed to facilitate clinical translation. A published risk model used 5 accessible clinical factors to predict the development of breast cancer-related lymphedema; this model included a patient's mammographic breast density as a novel predictive factor. Objective: To investigate the external validity of a previously reported 5-factor model by applying it to an independent cohort of patients with breast cancer. Design, Setting, and Participants: This prognostic study collected data on a longitudinal cohort of patients with predominantly early-stage breast cancer treated with curative intent at the Princess Margaret Cancer Centre in Toronto, Canada between February 1, 2010, and July 31, 2014, with a median (IQR) follow-up of 4.3 (2.4-7.6) years. The 5 factors (age, body mass index, breast density, nodal burden, and use of axillary lymph node dissection [ALND]) were used as input into the established regression-based model. The analysis was performed from July 2 through August 29, 2024. Exposure: Lymphedema after breast cancer treatment. Main Outcomes and Measures: Lymphedema-free survival (LFS) was analyzed using Kaplan-Meier analysis, and sensitivity, specificity, and accuracy performance metrics of predicting breast cancer-related lymphedema were calculated. Results: A total of 101 female patients (median [IQR] age, 54.8 [48.8-62.3] years) were included in the analysis. These patients had localized or locoregional breast cancer treated with primary lumpectomy (90 [89%]) or mastectomy (11 [11%]); 75 (74%) had no axillary biopsy or sentinel lymph node biopsy; 26 (26%) had undergone ALND; and 38 (38%) had received chemotherapy, 101 (100%) received radiotherapy, and 64 (63%) received hormone therapy. Kaplan-Meier analysis showed a 2-year LFS of 97.5% (95% CI, 94.0%-100.0%) vs 65.0% (95% CI, 47.1%-89.7%) for the low- vs high-risk groups as defined by the 5-factor model (P < .001). The model sensitivity was 0.83 (95% CI, 0.52-0.98), specificity was 0.89 (95% CI, 0.80-0.94), and accuracy was 0.88 (95% CI, 0.80-0.94) for predicting breast cancer-related lymphedema. Conclusions and Relevance: These findings validate the performance of a 5-factor risk model for its prediction of 2-year LFS. Future clinical translation of this model can help with identifying patients at the highest risk of breast cancer-related lymphedema to facilitate closer surveillance and/or preventive management to improve health outcomes and quality of life.
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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 | 0.077 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".