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Development of New BREAST-Q Scales to Measure the Experience of Breast Implant Illness

2022· article· en· W4320064136 on OpenAlexaboutno aff
Manraj Kaur, Andrea L. Pusic, Sylvie D. Cornacchi, Anne F. Klassen

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2022
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsnot available
Fundersnot available
KeywordsPromDelphi methodMedicineBreast cancerInclusion (mineral)Gold standard (test)Patient-reported outcomeDelphiBreast implantPsychologyMedical physicsQuality of life (healthcare)ImplantSurgeryNursingComputer scienceSocial psychologyInternal medicineCancer

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.273
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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