Prospective, randomized, controlled clinical study on single-incision laparoscopic cholecystectomy: an analysis of 449 cases from a single center
Bibliographic record
Abstract
BACKGROUND: Single-incision laparoscopic cholecystectomy (SILC), a minimally invasive alternative to conventional laparoscopic cholecystectomy (CLC), may improve postoperative recovery and cosmetic outcomes but faces concerns about complications and technical demands. This randomized controlled trial compares SILC and CLC using conventional laparoscopic instruments. MATERIALS AND METHODS: 891 patients were randomized to SILC (n = 449) or CLC (n = 442). Operative parameters, postoperative recovery, complications, and patient-reported outcomes were evaluated. Primary endpoints were operative time, blood loss, and complication rates. Secondary outcomes included hospital stay, pain scores, and cosmetic satisfaction (Vancouver scar scores). RESULTS: SILC showed similar operative time (55.11 ± 22.88 vs 51.81 ± 23.61 min, p = 0.907) and blood loss (10.89 ± 26.37 vs 10.14 ± 14.38 ml, p = 0.475) versus CLC. SILC patients had shorter hospitalization (1.94 ± 1.87 vs 2.25 ± 2.49 days, p < 0.001), lower pain scores (2.19 ± 0.88 vs 2.80 ± 0.75, p = 0.016), and better scar outcomes (2.41 ± 1.81 vs 3.54 ± 1.61, p = 0.020). Complication rates like bile leakage and hernias were marginally higher in SILC but not statistically significant. CONCLUSION: SILC is a safe, effective alternative to CLC, offering better postoperative recovery and cosmetic results. However, patient selection and surgical expertise are crucial to optimize outcomes and minimize complications.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".