Development of the BODY-Q Chest Module Evaluating Outcomes following Chest Contouring Surgery
Bibliographic record
Abstract
BACKGROUND: Plastic surgery to improve chest appearance is becoming increasingly popular. The BODY-Q is a patient-reported outcome instrument designed for weight loss and/or body contouring. In this article, the authors describe the development of a new module for masculinizing chest contouring surgery. METHODS: Qualitative methods were used to develop the BODY-Q Chest Module, which was subsequently field-tested in Canada, the United States, The Netherlands, and Denmark between June of 2016 and June of 2017. Participants were aged 16 years or older and seen for gynecomastia, weight loss, or transman chest surgery. Data were collected using either a Web-based application or paper questionnaire. Rasch measurement theory analysis was performed. RESULTS: The sample included 739 participants (i.e., 174 gynecomastia, 224 weight loss, and 341 gender-affirming). Rasch measurement theory analysis refined a 10-item chest scale and a five-item nipple scale. All items had ordered thresholds and good item fit, and scales evidenced reliability [i.e., person separation index and Cronbach alpha values were 0.95 and 0.98 (chest scale) and 0.87 and 0.94 (nipple scale), respectively]. Scores for both scales correlated more strongly with similar (satisfaction with the body) versus dissimilar (psychological and social function) BODY-Q scales. The mean scores for the chest and nipple scales were significantly higher (p < 0.001 on independent samples t tests) in participants who were postoperative compared with preoperative. CONCLUSION: This new BODY-Q Chest Module is a clinically meaningful and scientifically sound patient-reported outcome instrument that can be used to measure outcomes for masculinizing chest contouring surgery.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".