Axillary Web Syndrome in Breast Cancer: A Prevalent But Under-Recognized Postoperative Complication
Bibliographic record
Abstract
The purpose of this review is to define axillary web syndrome (AWS) and describe its diagnosis and management. The following databases were searched through July 2017: PubMed, EMBASE (OvidSP), Cumulative Index for Nursing and Allied Health Literature, Physiotherapy Evidence Database, and Cochrane Database of Systematic Reviews. Search terms included 'axillary web syndrome', 'axillary cording', and 'lymphatic cording'. 49 articles were identified; 8 did not relate to breast cancer, and 3 were not in English. Of the remaining articles, the majority were case reports, case series, or descriptive reviews. 2 systematic reviews were located as well as 1 randomized trial, 6 prospective cohort studies, and 2 retrospective cohort studies. Although a common sequela after axillary surgery for breast cancer staging, AWS has been poorly described in the medical literature as to the underlying pathophysiology, diagnosis, and management. Interventions range from patient education and simple reassurance that the syndrome will resolve spontaneously to active physical or manual therapies to maintain upper extremity range-of-motion, especially adequate motion for undergoing radiation therapy. Oncologists, breast surgeons, family physicians, and oncology nurse practitioners that work with patients with breast cancer should educate them about this prevalent complication and inform them preoperatively about what to anticipate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".