Development of a comprehensive prognostic model of lymphedema risk for breast cancer survivors.
Bibliographic record
Abstract
e23083 Background: Lymphedema is a side-effect of cancer treatment affecting up to 1 in 5 cancer survivors. It is associated with life-threatening medical complications and higher medical costs. To facilitate early detection and treatment of lymphedema in high-risk patients, improved prognostic modeling is required. Methods: The study cohort comprised of female breast cancer survivors at the Princess Margaret Cancer Centre during 2016-18. Using lymphedema diagnosis as the end-point, the Cox proportional-hazards model was applied to patient, disease, and treatment-related parameters to assess performance of established and putative novel prognostic factors for lymphedema. Concordance index (C-index) and Kaplan-Meier analyses were used to evaluate prognostic performance. Results: A total of 176 breast cancer survivors were included in the preliminary analysis. On univariable analysis, traditional treatment-related risk factors for lymphedema (e.g. axillary lymph node dissection, number of lymph nodes dissected, use of radiotherapy) were significant (p < 0.05). Additionally, other treatment-related factors (e.g. radiation boost, supraclavicular radiation), disease factors (e.g. tumor size, number of positive lymph nodes, cancer stage), and patient/biological factors (e.g. developing a surgical seroma) were significant (p < 0.05). Multivariable analysis revealed one traditional treatment-related factor (i.e. number of lymph nodes dissected) and other treatment details (e.g. radiation boost, axillary/supraclavicular radiation) to be the most significant factors. Combining radiation treatment details with traditional treatment-related lymphedema risk factors improved the C-index from 0.609 to 0.710 and separated patients into high- and low-risk groups with 2-year lymphedema-free survivals of 26% (15-44%) and 85% (74-97%) respectively (p < 0.001). Conclusions: Prognostication of lymphedema risk can be improved by considering radiation treatment details, including use of radiation boost and site of radiotherapy. Biological factors, such as developing a surgical seroma, may reveal predisposition towards edema. Validation on an independent data set is being completed. Prospective validation is also required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".