Minimally Invasive Cholecystolithotomy to Treat Cholecystolithiasis in Children: A Single-center Experience With 23 Cases
Bibliographic record
Abstract
OBJECTIVE: Minimally invasive cholecystolithotomy is recently popularized treatment that may offer advantages over laparoscopic cholecystectomy, especially in China. However, there are few reports concerning the use of this technique in the pediatric population. This report describes our initial experience with minimally invasive cholecystolithotomy using laparoscopy combined with choledochoscopy to treat cholecystolithiasis in children. MATERIALS AND METHODS: A retrospective review of 23 pediatric patients with cholecystolithiasis who underwent minimally invasive cholecystolithotomy using laparoscopy combined with choledochoscopy from January 2009 to December 2015 was performed. RESULTS: The operations were successful in all 23 cases. None required conversion to conventional laparoscopic cholecystectomy. The average operative time was 68 minutes (range, 45 to 97 min). The average bleeding volume during surgery was 30 mL (range, 10 to 55 mL). The average length of hospital stay was 5.2 days (range, 3 to 7 d). There were no perioperative complications. All patients were followed for 9 to 12 months without any obvious gastrointestinal symptoms. None had a recurrence of stones in the gall bladder. CONCLUSIONS: Minimally invasive cholecystolithotomy using laparoscopy combined with choledochoscopy is a safe and viable technique that may be used successfully in pediatric surgery.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".