The Impact of Abdominal Body Contouring Surgery on Physical Function After Massive Weight Loss: A Pilot Prospective Matched Comparison
Bibliographic record
Abstract
BACKGROUND: Many individuals develop excess skin (ES) following massive weight loss (MWL). Patient-reported outcomes demonstrate that abdominal ES negatively impacts perceived physical function which is improved by abdominal body contouring surgery (ABCS). However, the effect of ABCS on objective measures of physical function is unknown. OBJECTIVES: The aim of this study was to examine the impact of ABCS on objective measures of physical function in individuals who have undergone MWL. METHODS: Patients who have undergone MWL with abdominal ES (grade, ≥2) underwent the following physical function assessments: 9-item modified physical performance test (mPPT), chair stand, star excursion balance test (SEBT), timed up and go (TUG), modified agility T test, and 6-minute walk test (6-MWT). Perception of physical exertion and BODY-Q questionnaire scales were also collected. Nonsurgical controls (n = 21) and those who had undergone ABCS (n = 6) after the first visit performed a second physical function assessment 8 to 12 weeks later to allow for postoperative healing. RESULTS: No ceiling or floor effect was detected for any physical function measure. The intraclass correlation coefficient was 0.78 (95% CI, 0.44, 0.91) for the mPPT and >0.80 for all other measures. The effect sizes were 0.74 (75% CI, 0.19, 1.28) for the mPPT, 0.54 (75% CI, 0.00, 1.08) for the SEBT, -0.63 (75% CI, -1.17, -0.09) for the modified agility T test, and 0.79 (75% CI, 0.24, 0.13) for the 6-MWT. CONCLUSIONS: The mPPT and tests involving dynamic balance, agility, and walking were reliable and showed medium to large effect sizes, suggesting that these tests may be sensitive to change following ABCS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".