Laparoscopic sleeve gastrectomy at a new bariatric surgery centre in Canada: 30-day complication rates using the Clavien–Dindo classification
Bibliographic record
Abstract
BACKGROUND: 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 NL. This retrospective study examined 30-day complication rates in more than 200 consecutive patients who underwent laparoscopic sleeve gastrectomy (LSG) between May 2011 and February 2014. METHODS: We performed a chart review and collected data on 30-day postoperative 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 complications. RESULTS: We reviewed the charts of the first 209 patients to undergo LSG. The mean body mass index was 49.2, 81% were women 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%) and 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% (2 leaks, 1 stricture and 1 fistula). CONCLUSION: Our results support the feasibility of safely performing LSG surgery at bariatric centres completing fewer than 125 procedures annually.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".