Bariatric surgery - complications and safety of laparoscopic sleeve gastrectomy performed at a lower-volume tertiary care center
Bibliographic record
Abstract
Newfoundland and Labrador (NL) has the highest rate of obesity in Canada, prompting the establishment of a bariatric surgery program at the Health Sciences Centre in St. John’s, NL. This retrospective study examined less than 30-day complication rates in over 200 consecutive patients between May 2011 and February 2014 who underwent laparoscopic sleeve gastrectomy (LSG). A chart review was performed and data collected on less than 30-day post-operative complications. Complications were graded and reported using the Clavien-Dindo Classification. Grades I and II were defined as minor and grades III and higher were defined as major. The first 209 LSG patients were reviewed. The mean BMI was 49.2 kg/m2, 81% were female and the average age was 43 years. Comorbidities included: hypertension (55.0%), obstructive sleep apnea (46.4%), dyslipidemia (42.1%), diabetes (37.3%), osteoarthritis (36.4%), cardiovascular disease with previous cardiac stents (5.3%). Furthermore, 38.3% of patients reported psychiatric diagnoses such as depression and anxiety. The overall 30-day complication rate was 15.3%. The complication rate for minor complications was 13.4% and for major complications was 1.9% (i.e., two leaks, one stricture and one fistula). Our results support the feasibility of safely performing LSG surgery at bariatric centers performing fewer than 125 procedures annually.
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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 | 0.000 |
| 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 | 0.000 |
| 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".