Outcomes of Laparoscopic Cholecystectomy for Biliary Dyskinesia in Children
Bibliographic record
Abstract
PURPOSE: To determine the outcomes of laparoscopic cholecystectomy as a treatment for biliary dyskinesia in children. METHODS: With ethics approval, a retrospective chart review was performed on children (<21 years) at a single center diagnosed with biliary dyskinesia (defined as gallbladder ejection fraction [EF] <35% and/or pain with cholecystokinin [CCK] on cholescintigraphy, in the absence of gallstones or cholecystitis on ultrasound) and treated with laparoscopic cholecystectomy between March 2010 and February 2016. Demographic, medical history, diagnostic imaging, pathology, and outcome data were collected and analyzed based on degree of symptom resolution. RESULTS: ). 181/206 (87.9%) had EF <35%. CCK reproduced symptoms in 149/177 (84.2%). 34/215 (15.8%) were lost to follow-up. Median follow-up time was 2.7 weeks. Pain improved in 162/181 (89.5%). Chronic cholecystitis was found in 183/213 (85.9%) and unexpected cholelithiasis in 4/213 (1.9%) on pathology. Postoperatively, 6/181 (3.3%) had wound infections and 8/181 (4.4%) required common bile duct stents for the following indications: 6 sphincter of Oddi dysfunction, 1 choledocholithiasis, and 1 stricture. Virgin abdomen (odds ratio [OR] 4.03, confidence interval [95% CI] 1.12-14.53, P = .0460) and follow-up <6 months (OR 7.35, 95% CI 2.68-20.21, P = .0002) were associated with better outcomes. CONCLUSIONS: Laparoscopic cholecystectomy is safe and effective in symptom resolution for biliary dyskinesia in children. Virgin abdomen and follow-up <6 months were associated with better outcomes. Prospective long-term studies comparing surgical and nonoperative management of biliary dyskinesia are required to determine the utility of cholecystectomy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".