Predictors of arm morbidity following breast cancer surgery
Bibliographic record
Abstract
OBJECTIVE: Arm morbidity post-breast cancer surgery is increasingly being recognized as a chronic problem for some women following breast cancer surgery. The purpose of this study was to examine demographic, disease, and treatment-related predictors of a comprehensive array of chronic arm morbidity (pain, lymphedema, functional disability, and range of motion) post-breast cancer surgery. METHODS: Women (n=316) with a non-metastatic primary diagnosis of breast cancer were accrued from cancer centers in four Canadian cities. Patients completed a clinical assessment and measures of arm morbidity at 6-12 months post-breast cancer surgery. The independent variables in the MANOVA to predict arm morbidity included: Lymph node management type, number of axillary nodes dissected, type of surgery, disease stage, presence of post-operative infection, radiation to the axilla, body mass index (BMI), assessment time post-surgery, education, and partner status. RESULTS: Pain was significantly predicted by axillary lymph node management, lack of a partner, and post-operative infection; lymphedema by axillary lymph node management, number of axillary nodes dissected, radiation to the axilla, and having a modified radical mastectomy; functional disability by post-operative infection and high BMI; and restricted external rotation by axillary lymph node management, low educational attainment, and advanced disease. CONCLUSION: Comprehensive behavioral management and rehabilitation programs are needed to treat arm morbidity following breast cancer surgery. These programs should address the full scope of symptoms and associated psychosocial and functional sequelae.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".