Predictors of Risk and Success of Obesity Surgery
Bibliographic record
Abstract
BACKGROUND: Obesity surgery has proven successful for weight loss and the resolution of comorbidities. There is, however, little evidence on its success and the risk of complications when considering age of onset of obesity (AOO), years of obesity (YOO), preoperative body mass index (BMI), Edmonton obesity staging system (EOSS) score, and age as possible predictors of weight loss, the resolution of comorbidities, and the risk of complications. METHODS: Patients who underwent Roux-en-Y gastric bypass (RYGB) and laparoscopic sleeve gastrectomy (LSG) from a prospective database were analyzed. Multiple regression analyses were used to predict comorbidities and their resolution, percentage excess weight loss (%EWL) and total weight loss (%TWL) 12 months after surgery, and the risk of complications using the predictors AOO, YOO, age, EOSS, and BMI. RESULTS: 180 patients aged 46.8 ± 11.1 years with a preoperative BMI 49.5 ± 7.5 were included. The number of preoperative comorbidities was higher with older age (β = 0.054; p = 0.023) and a greater BMI (β = 0.040; p = 0.036) but was not related to AOO and YOO. Patients with AOO as a child or adolescent were more likely to have an EOSS score of ≥2. Greater preoperative BMI was negatively associated with %EWL (β = -1.236; p < 0.001) and older age was negatively associated with %TWL (β = -0.344; p = 0.020). Postoperative complications were positively associated with EOSS score (odds ratio [OR] 1.147; p = 0.042) and BMI (OR 1.010; p = 0.020), but not with age. AOO and YOO were not related to postoperative outcome. CONCLUSION: Greater BMI was associated with a lower %EWL and age was associated with a low %TWL. YOO and AOO did not influence outcome. Age, BMI, and EOSS score were the most important predictors for risk and success after obesity surgery. Surgery should be performed early enough for optimal outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".