Laparoscopic Surgery Compared With Open Surgery Decreases Surgical Site Infection in Obese Patients
Bibliographic record
Abstract
OBJECTIVE: To compare surgical site infections rate in obese patients after laparoscopic surgery with open general abdominal surgery. BACKGROUND: In mixed surgical populations, surgical site infections are fewer in laparoscopic surgery than in open surgery. It is not clear if this is also the case for obese patients, who have a higher risk of surgical site infections than nonobese patients. METHODS: MEDLINE, Embase, and The Cochrane library (CENTRAL) were searched systematically for studies on laparoscopic surgery compared with open abdominal surgery. Randomized controlled trials (RCTs) and observational studies reporting surgical site infection in groups of obese patients (body mass index ≥ 30) were included. Separate meta-analyses with a fixed effects model for RCTs and a random effects model for observational studies were performed. Methodological quality of the included studies was assessed according to the Cochrane method and the Newcastle-Ottawa Scale. RESULTS: Eight RCTs and 36 observational studies on bariatric and nonbariatric surgery were identified. Meta-analyses of RCTs and observational studies showed a significantly lower surgical site infection rate after laparoscopic surgery (OR = 0.19; 95% CI [0.08-0.45]; P = 0.0002 and OR = 0.33; 95% CI [0.26-0.42]; P = 0.00001). Sensitivity analyses to assess the impact of selection and detection bias confirmed the significant estimates with acceptable heterogeneity. No publication bias was present for the observational studies. CONCLUSIONS: Laparoscopic surgery in obese patients reduces surgical site infection rate by 70%-80% compared with open surgery across general abdominal surgical procedures. Future efforts should be focused on further development of laparoscopic surgery for the growing obese population.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".