Using the BODY-Q to Understand Impact of Weight Loss, Excess Skin, and the Need for Body Contouring following Bariatric Surgery
Bibliographic record
Abstract
BACKGROUND: A consequence of bariatric surgery is redundant skin for most patients. The authors measured health-related quality of life and appearance following bariatric surgery in relation to weight loss, excess skin, and need for body contouring. METHODS: The sample included Canadian participants from the BODY-Q field-test study recruited between November of 2013 and July of 2014. Participants were invited to complete BODY-Q scales and questions to assess weight loss, amount of excess skin, and need for body contouring between June 7, 2016, and November 29, 2016. RESULTS: Two hundred fourteen participants responded (75 percent response rate). Of the 210 who underwent bariatric surgery, most were left with excess skin [n = 196 (93 percent)] and needed body contouring [n = 168 (80 percent)]. Higher percentage total weight loss correlated with more excess skin (r = 0.24, p = 0.001), the need for more body contouring procedures (r = 0.29, p < 0.001), and (worse) scores on seven of 13 BODY-Q scales. Having redundant skin correlated with more physical symptoms (r = 0.31, p < 0.001), the need for more body contouring procedures (r = 0.62, p < 0.001), and lower scores on 12 BODY-Q scales. The need for more body contouring procedures correlated with more physical symptoms (r = 0.23, p = 0.001) and lower scores on 12 BODY-Q scales. CONCLUSIONS: Excess skin after bariatric surgery is a disabling problem. Additional research using the BODY-Q is needed to determine improvements that can be achieved following body contouring.
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".