A Systematic Review of Post-Operative Breast Undergarments for Patients with Breast Cancer
Bibliographic record
Abstract
Background: Breast cancer is the most common malignancy among Canadian women, affecting approximately 1 in 8 women over their lifetime. Despite advancements in surgical management, many patients experience significant post-operative discomfort. This systematic review evaluates existing post-operative breast undergarments and highlights design shortcomings that impact patient recovery. Methods: A systematic search was conducted using Ovid MEDLINE and EBSCO CINAHL Complete for literature published between January 1, 1946, and April 30, 2025. The search strategy included a combination of Medical Subject Headings (MeSH) and keywords related to breast neoplasms, mastectomy, post-operative care, and support garments. A total of 126 studies were identified, with 11 duplicates removed. After screening, 29 relevant studies were included. Results: We identified various shortcomings in conventional post-operative garments, including inadequate storage for surgical drains, poor thermoregulation, and materials causing dermatological irritation. Issues such as non-adjustable straps, discomfort from under-wires, and a lack of accommodations for breast asymmetry and prosthesis fitting were prevalent. Although newer designs have addressed some of these issues by incorporating features like customizable compression and improved front fasteners, significant gaps remain. Conclusion: Current post-operative undergarments inadequately address key aspects of recovery for breast cancer patients, particularly in the early post-operative period. There is a clear demand for garments with enhanced comfort, adjustable support, better drain management, and skin-friendly materials. Further design innovations and clinical evaluations are required to optimize post-operative care.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".