Axillary Web Syndrome in Newly Diagnosed Individuals After Surgery for Breast Cancer: Baseline Results From the AMBER Cohort Study
Bibliographic record
Abstract
Purpose: To examine potential associations between post-surgical axillary web syndrome (AWS) and demographic, medical, surgical, and health-related fitness variables in newly diagnosed individuals with breast cancer. Method: Participants were recruited between 2012 and 2019. Objective measures of health-related fitness, body composition, shoulder range of motion (ROM) and function, and AWS were performed within 3 months of breast cancer surgery. Results: AWS was identified in 243 (17.3%) participants and was associated with poorer shoulder ROM and function, and higher pain compared with women without AWS. Multivariable logistic regression analysis identified axillary lymph node dissection versus sentinel lymph node biopsy (OR 3.97; 95% CI: 2.62, 6.03), mastectomy versus breast-conserving surgery (OR 1.60; 95% CI: 1.17, 2.19), lower versus higher total percentage body fat (OR 1.60; 95% CI: 1.10, 2.34), and earlier versus later time from surgery (OR 1.56; 95% CI: 1.10, 2.23) as significantly associated with a higher odds of AWS. Higher cardiorespiratory fitness (OR 1.04; 95% CI: 1.01, 1.08) and university or higher education (OR 1.47; 95% CI: 1.1, 2.00) were also associated with higher odds of presenting with AWS. Conclusions: Findings highlight the need for increased awareness of AWS to facilitate early detection and physiotherapy intervention in the early post-surgical period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".