What patient factors influence bariatric surgery outcomes? A multiple regression analysis of Ontario Bariatric Registry data
Bibliographic record
Abstract
Background: As bariatric surgery evolves and gains popularity, statistical analysis of its outcomes could improve the process of decision-making and risk assessment. This study aimed to evaluate the influence of age and other factors on bariatric surgery outcomes in order to improve patient selection and outcomes. Methods: We analyzed data from the Ontario Bariatric Registry to evaluate the influence of age and 10 other factors on early (< 90 d) and 1-year surgical outcomes among patients aged 18 years or older who underwent laparoscopic Roux-en-Y gastric bypass (LRYGB) or laparoscopic sleeve gastrectomy (LSG) between January 2010 and May 2013. Early outcomes included composite adverse events and readmission. The 1-year outcomes included percent excess body weight loss (%EBWL), and remission of diabetes mellitus and hypertension. We performed multiple regression analysis to identify independent variables that influenced these outcomes. Results: We identified 3166 patients (2655 women [83.9%] and 511 men [16.1%], mean age 44.8 yr, mean body mass index [BMI] 48.4) who underwent LRYGB (2839 [89.7%]) or LSG (327 [10.3%]) over the study period and completed their 1-year follow-up. Preoperative American Society of Anesthesiologists (ASA) score and history of angina were independent variables that influenced the composite adverse event outcome. Obstructive sleep apnea was the only factor that influenced early readmission. The independent factors that influenced %EBWL were age, type of surgery, BMI and baseline glycosylated hemoglobin (HbA1c) level: age was found to influence hypertension remission, and HbA1c level and obstructive sleep apnea were found to influence diabetes remission. Conclusion: Complications after bariatric surgery can be predicted by preoperative ASA score and history of angina; patient age was not related to an increase in postoperative complications. These factors could help both surgeon and patient make appropriate surgical decisions.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".