The Edmonton Obesity Staging System Predicts Postoperative Complications After Abdominoplasty
Bibliographic record
Abstract
BACKGROUND: This study investigates the relationship between Edmonton Obesity Staging System (EOSS) and the occurrence of postoperative complications after abdominoplasty in massive weight loss patients. METHODS: A single-institution retrospective review of patients undergoing abdominoplasty between 2009 and 2019 after massive weight loss. Demographic data, laboratory findings, known risk factors for postoperative complications, as well as data on major and minor complications were extracted from the patient charts. Logistic regression models were used to investigate the relationship between the variables. RESULTS: Four hundred and five patients were included in the study. The prevalence of EOSS stages was: 0 (no comorbidities, N = 151, 37%), 1 (mild conditions, N = 40, 10%), 2 (moderate conditions, N = 149, 36%) and 3 (severe conditions, N = 70, 17%). Regression analysis showed that, controlling for body mass index (BMI), BMI Δ (maximal BMI - BMI at presentation), bariatric surgery, volume of resected tissue, and duration of surgery, EOSS stage significantly associated with the occurrence of postoperative complications. Compared with EOSS stage 0, EOSS stages 2 and 3 patients were associated with significantly more minor and major complications, respectively. The volume of resected tissue, BMI Δ, and age were associated with the occurrence of major complications. A regression model of comorbidities comprising the EOSS revealed a significant association of variables diabetes mellitus and hypertension with the occurrence of postoperative complications. CONCLUSIONS: Edmonton Obesity Staging System is a robust predictor of postoperative complications in abdominoplasty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".