Laparoscopic Surgery Compared With Open Surgery Decreases Surgical Site Infection in Obese Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it