General population normative scores for interpreting the <scp>BODY‐Q</scp>
Bibliographic record
Abstract
Summary The BODY‐Q is a patient‐reported outcome measure used to assess outcomes in patients undergoing weight loss and/or body contouring surgery (BC) following massive weight loss. Normative values for the BODY‐Q are needed to improve data interpretation and enable comparison. Thus, the aim of this study was to determine normative values for the BODY‐Q. Participants were recruited internationally through two crowdsourcing platforms. The participants were invited to complete the BODY‐Q scales through an URL link provided within the crowdsourcing platforms. General linear analyses were performed to compare normative means between countries and continents adjusted for relevant covariates. Normative reference values were stratified by age, body mass index (BMI), and gender. The BODY‐Q was completed by 4051 (2052 North American and 1999 European) participants. The mean age was 36 years (±14.7 SD) and ranged from 17 to 76 years, the mean BMI was 26.4 (±6.7 SD) kg/m2, and the sample consisted of 1996 (49.3%) females and 2023 (49.9%) males. Younger age and higher BMI were negatively associated with all BODY‐Q scales (p < .001). This study provides normative values for the BODY‐Q scales to aid in the interpretation of BODY‐Q scores in research and clinical practise. These values enable us to understand the impact of weight loss and BC on patients' lives.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".