Supportive, Fitted, and Comfortable Bras for Individuals with Atypical Breast Shape/Size: Review of the Challenges and Proposed Roadmap
Bibliographic record
Abstract
Individuals with atypical breast shape/size often find it quite challenging to obtain a comfortable, supportive, and fitted bra off-the-shelf. They include people with very large breasts, who have significant breast asymmetry, and/or have undergone mastectomy or mammoplasty. This paper provides insights in their challenges and attempts to fill the gap in terms of critical review of the current state of knowledge around the topic of bras. Poor and ill fitted bras are associated with breast, chest and shoulder pain, embarrassment, and an overall reduction in quality of life among others. Building upon the advantages and limitations of solutions to improve the fit, support and comfort of bras found in the literature, this paper proposes strategies to solve these challenges. As the problem is multidisciplinary, a human-centered interdisciplinary approach is key to ensure that all aspects are considered at all stages of the process. A modular design allows selecting the fabric characteristics based on the requirements of each bra part. In terms of materials, stretch woven fabrics offer a large potential in the production of bras to enhance the support provided by areas such as the under band and back panels. Bespoke manufacturing takes into account the specificities of each individual. The road map proposed here will contribute to enhance the quality of life of individuals with atypical breast shape/size.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".