Improving the Impact of BODY-Q Scores Through Minimal Important Differences in Body Contouring Surgery: An International Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The BODY-Q is a widely used patient-reported outcome measure for comprehensive assessment of treatment outcomes specific to patients undergoing body contouring surgery (BCS). However, for the BODY-Q to be meaningfully interpreted and used in clinical practice, minimal important difference (MID) scores are needed. A MID is defined as the smallest change in outcome measure score that patients perceive as important. OBJECTIVES: The aim of this study was to determine BODY-Q MID estimates for patients undergoing BCS to enhance the interpretability of the BODY-Q. METHODS: Data from an international, prospective cohort from Denmark, Finland, Germany, Italy, the Netherlands, and Poland were included. Two distribution-based methods were used to estimate MID: 0.2 standard deviations of mean baseline scores and the mean standardized response change of BODY-Q scores from baseline to 3 years postoperatively. RESULTS: A total of 12,554 assessments from 3237 participants (mean age 42.5 ± 9.3 years; BMI 28.9 ± 4.9 kg/m2) were included. Baseline MID scores ranged from 1 to 5 on the health-related quality of life (HRQL) scales and 3 to 6 on the appearance scales. The estimated MID scores from baseline to 3-year follow-up ranged from 4 to 5 for HRQL and from 4 to 8 on the appearance scales. CONCLUSIONS: The BODY-Q MID estimates from before BCS to 3 years postoperatively ranged from 4 to 8 and are recommended for interpretation of patients' BODY-Q scores, evaluation of treatment effects of different BCS procedures, and calculation of sample size for future studies.
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 | 0.003 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".