Population-based trend analysis of 2813 patients undergoing laparoscopic sigmoid resection
Bibliographic record
Abstract
BACKGROUND: The use of laparoscopic sigmoid resection for diverticular disease has become increasingly popular. The objective of this trend analysis was to assess whether clinical outcomes following laparoscopic sigmoid resection for diverticular disease have improved over the past 10 years. METHODS: The analysis was based on the prospective database of the Swiss Association of Laparoscopic and Thoracoscopic Surgery. Some 2813 patients undergoing elective laparoscopic sigmoid resection for diverticular disease from 1995 to 2006 were included. Unadjusted and risk-adjusted analyses were performed. RESULTS: Over time, there was a significant reduction in the conversion rate (from 27.3 to 8.6 per cent; P(trend) < 0.001), local postoperative complication rate (23.6 to 6.2 per cent; P(trend) = 0.004), general postoperative complication rate (14.6 to 4.9 per cent; P(trend) = 0.024) and reoperation rate (5.5 to 0.6 per cent; P(trend) = 0.015). Postoperative median length of hospital stay significantly decreased from 11 to 7 days (P(trend) < 0.001). CONCLUSION: This first trend analysis in the literature of clinical outcomes after laparoscopic sigmoid resection, based on almost 3000 patients, has provided compelling evidence that rates of postoperative complications, conversion and reoperation, and length of hospital stay have decreased significantly over the past 10 years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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 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".