Psychometric Validation of the BODY-Q in Danish Patients Undergoing Weight Loss and Body Contouring Surgery
Bibliographic record
Abstract
Background: A well-developed patient-reported outcome instrument is needed for use in Danish bariatric and body contouring patients. The BODY-Q is designed to measure changes in important patient outcomes over the entire patient journey, from obesity to post-body contouring surgery. The current study aims to psychometrically validate the BODY-Q for use in Danish patients. Methods: The process consisted of 3 stages: translation and linguistic validation, field-test, and data analysis. The translation was performed in accordance with the International Society for Pharmacoeconomics and Outcomes Research and World Health Organization guidelines, and field-test data were collected in 4 departments in 2 different hospitals. Field-test data were analyzed using Rasch Measurement Theory. Results: A total of 495 patients completed the Danish BODY-Q field-test 1–4 times, leading to a total of 681 assessments with an overall response rate at 76%. Cronbach α values were ≥ 0.90, and person separation index values were in general high. The Rasch Measurement Theory analysis provided broad support for the reliability and validity of the Danish version of the BODY-Q scales. Item fit was outside the criteria for 34 of 138 items, and of these, 21 had a significant chi-square P value after Bonferroni adjustment. Most items (128 of 138) had ordered thresholds, indicating that response options worked as intended. Conclusion: The Danish version of the BODY-Q is a reliable and valid patient-reported outcome instrument for use in Danish bariatric and body contouring patients.
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 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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".