Comparison of the Moorehead‐Ardelt quality of life questionnaire and the BODY‐Q in Danish patients undergoing weight loss and body contouring surgery
Bibliographic record
Abstract
This study compared the measurement properties of the Moorehead-Ardelt quality of life questionnaire-II (MAQOL-II) and the BODY-Q, to determine which was more suitable for measuring patient-reported outcome (PRO) in Danish bariatric surgery (BS) and body contouring surgery (BC) patients. We examined content validity against the COSMIN guidelines and compared psychometric performance using Rasch measurement theory methods and criteria. MAQOL-II data were obtained from the Danish Bariatric Surgery Database from September 2010 to November 2017, and BODY-Q data were collected from June 2015 to March 2018. The MAQOL-II failed to meet recommended standards for content validity, while all criteria were met for the BODY-Q. A total of 16 965 MAQOL-II and 2259 BODY-Q assessments were obtained. A random sample was selected from the MAQOL-II dataset to match the BODY-Q sample. Psychometrically, the BODY-Q performed better than the MAQOL-II. For example, Cronbach's α was 0.82 for the MAQOL-II vs ≥0.90 for all BODY-Q scales. Fifty percent (3/6) of MAQOL-II items had disordered thresholds, while all BODY-Q items had ordered thresholds (123/123). Poor item fit was revealed for 17% (1/6) of MAQOL-II and 8% (10/123) of BODY-Q items. For scale reliability, person separation index was 0.79 for the MAQOL-II and 0.88 (0.81-0.93) for the BODY-Q. In conclusion, the MAQOL-II does not meet today's standards for a rigorously developed PRO measure. The BODY-Q, on the other hand, gives substantial, accurate and interpretable measurement and should be recommended for use in PRO in BS and BC 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".