Development of New BREAST-Q Scales to Measure the Experience of Breast Implant Illness
Bibliographic record
Abstract
PURPOSE: Breast implant illness (BII) is a term used to describe a constellation of systemic symptoms reported by women post-implant-based breast reconstruction (IBRR). Research is underway to understand the pathophysiology of BII and no diagnostic tests currently exist. Hence, understanding the patient experience of BII is of utmost importance. Patient-reported outcome measures (PROMs) are tools designed to facilitate the inclusion of patient voice in clinical care. The purpose of this study is to develop a long- and short-form of BII symptom severity questionnaires. These new questionnaires will be part of the BREAST-Q – a rigorous, validated, gold standard PROM for breast cancer surgery. METHOD: An international, multistep, multiphase approach consistent with established PROM development guidelines will be used. In Step 1, a review of published and gray literature was conducted to identify a comprehensive list of symptoms associated with BII, followed by an online, Delphi survey and consensus meeting with stakeholders (patients, clinicians, researchers, and a regulatory member) to identify top 20,10 and 5 symptoms associated with BII. In Step 2, we conducted a web-scraping study of 9 publicly available BII-specific web forums to extract de-identified BII-relevant posts and comments. In Step 3, using an interview guide, we conducted in-depth, one-on-one interviews with women with BII-like symptoms recruited through patient partners from the United States and Canada. The interviews were audio-recorded and transcribed verbatim. The data from Steps 2 and 3 were analyzed line-by-line to extract relevant concepts and constant comparison was used to develop a conceptual framework for BII and an item pool. The item pool was used to develop the draft of the long- and short-form of the BREAST-Q BII symptom severity scales. RESULTS: In Step 1, 44 symptoms were reviewed by the Delphi panel (N=25) and a consensus was reached on the top 19 and 6 symptoms to be included in the long- and short-form of the scales. In Step 2, we found that pre-implant surgery, women were concerned about the risks of developing BII with certain types of implants. Women who were experiencing BII-like symptoms post-implants described their symptoms, were worried about worsening of symptoms and identified the need for more resources on BII and explant surgery. In Step 3, 20 women (age, 32-58y) elaborated on the health-related quality of life impact of BII, including appearance and body image (eg, skin/hair changes, weight loss/gain), and physical (eg, fatigue, pain, gastrointestinal issues), psychological (eg, anxiety, depression), social (eg, participation in recreation, work), and sexual (eg, vaginal dryness, low libido) well-being. Participants’ words were used to develop draft versions of the BREAST-Q BII symptom severity scales. CONCLUSION: The BREAST-Q BII symptom severity scales are designed to be used in clinical practice and research to monitor and evaluate BII from the patient perspective and may be used for patient education pre-IBBR. The next steps in the development involve pilot testing and refining of draft scales followed by a field test with a large sample to establish psychometric properties.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".