Reply: Treatment of Severe Gynecomastia after Massive Weight Loss: Analysis of Long-Term Outcomes Measured with the Italian Version of the BODY-Q
Bibliographic record
Abstract
Sir: We would like to congratulate Barone and colleagues on their rigorous efforts to translate and culturally adapt the BODY-Q into the Italian language.1 The BODY-Q is a patient self-reported outcome measure developed for patients who undergo weight loss or body contouring.2 The BODY-Q is composed of 18 independently functioning scales that measure three domains (i.e., appearance, quality of life, and experience of health care) and a 10-item obesity-specific symptom checklist. Based on patient and expert feedback, several new appearance scales were recently added, namely, chest and nipple scales for men undergoing chest contouring surgery and a stretch marks scale. The BODY-Q, original developed in English, now has 10 translations available (i.e., Danish, Dutch, Finnish, French, German, Italian, Norwegian, Polish, Spanish, and Swedish), facilitating widespread uptake in research and clinical practice. When a patient-reported outcome measure is adapted to a country or language, it is essential to perform a thorough translation and linguistic validation. There are numerous methods in the literature available to guide the translation process.3 To develop the Danish translation, our team combined the widely used guidelines outlined by the International Society for Pharmacoeconomics and Outcomes Research,4 with supplemental steps outlined by the World Health Organization.5 This combined approach included the following steps: We obtained permission to use the BODY-Q, invited the developers to the study, and developed explanations for the concepts of the BODY-Q. Two independent forward translations (both translators had Danish as their first language and were fluent in English) followed by harmonization, leading to Danish version 1. Backward translation (the translator had English as their first language and was fluent in Danish). Comparison of the original BODY-Q and back-translated versions, and harmonization, leading to Danish version 2. An expert panel meeting held with the participation of all translators and clinicians (bariatric and body contouring surgeons) to ensure that the translated version was clinically relevant. Harmonization after expert panel meeting led to Danish version 3. Cognitive patient interviews performed to determine whether the translation was understandable and relevant for all patients which, following adjustments, led to Danish version 4. Danish version 4 was tested in further cognitive patient interviews to determine whether changes were appropriate. Finally, the Danish translated BODY-Q was independently proofread by two clinicians, leading to the final Danish version of the BODY-Q.6 To translate the BODY-Q into Italian, Barone and colleagues also followed the rigorous multistep process that combined the International Society for Pharmacoeconomics and Outcomes Research and World Health Organization guidelines. We agree with Barone et al. that cognitive interviews for patients should be completed before the Italian version of the BODY-Q is implemented in clinical practice. Patient-reported outcome measures are increasingly being used in clinical practice to measure patient outcomes and to inform shared decision-making. Patient-reported outcome measure data are also used to inform policy decisions, in development of patient education programs, and for comparative effectiveness research. A thorough linguistic and psychometric validation of a patient-reported outcome measure following rigorous guidelines, such as those put forth by the International Society for Pharmacoeconomics and Outcomes Research and the World Health Organization, provides a robust basis for all the above uses and allows for future international comparative research of patient-reported outcome data. DISCLOSURE The BODY-Q is owned by McMaster University and Memorial Sloan-Kettering Cancer Center. Drs. Klassen and Pusic are co-developers of the BODY-Q and, as such, could potentially receive a share of any license revenues as royalties based on their institution’s inventor sharing policy. The authors declare that they have no competing interests. Lotte Poulsen and Manraj Kaur have no financial disclosures to report. Lotte Poulsen, M.D.Department of Plastic SurgeryOdense University HospitalOdense, Denmark Manraj N. Kaur, P.T., M.Sc.(Rehab.)School of Rehabilitation ScienceMcMaster UniversityHamilton, Ontario, Canada Andrea L. Pusic, M.D., M.Sc.Brigham and Women’s HospitalBoston, Mass. Anne F. Klassen, D.Phil.Department of PediatricsMcMaster UniversityHamilton, Ontario, Canada
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".