Body Contouring Surgery Improves Long-Term Satisfaction with Appearance and Health-Related Quality of Life after Bariatric Surgery
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes are crucial in bariatric surgery (BaS) and body contouring surgery (BC) because patients' goals include improvement in appearance and health-related quality of life (HR-QOL). The BODY-Q is a patient-reported outcome measure developed to measure change in satisfaction with appearance and HR-QOL in BaS and BC patients. The aim of this study was to examine BODY-Q scores over the entire weight loss journey, and to investigate the impact of BC after BaS. METHODS: Patients completed the BODY-Q before and after BaS and BC at four hospital departments in Denmark between 2015 and 2019. Cross-sectional scores were analyzed by phase of weight loss journey using one-way analysis of variance. Scores for patients who provided longitudinal assessments were analyzed using repeated measures analysis of variance and paired t test. The impact of BC was examined over time after BaS, using an independent t test from before BaS through more than 7 years after BaS. RESULTS: The study included 1527 patients who provided 2285 BODY-Q assessments. The cross-sectional analysis by phase of weight loss journey showed higher scores after BaS, lower scores before BC, and highest-level scores after BC. The longitudinal analysis showed higher postoperative mean scores compared with preoperative scores for both BaS and BC. The analysis over time after BaS revealed lower mean scores in patients who did not receive BC. CONCLUSION: The authors' results provide evidence of the positive impact of BaS and BC on patients' lives and emphasize the importance of considering BC to finalize the weight loss journey, as it helps to maintain improvements in appearance and HR-QOL. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".