Evaluation of complications of laparoscopic cholecystectomy in a single centre experience
Bibliographic record
Abstract
Background: Laparoscopic cholecystectomy first introduced by Muhe in 1985 followed by Muret in1987, it is becoming more popular and now it is gold standard for symtomatic gallstone disease. Objective: To determine the complication of Laparoscopic cholecystectomy in our series. Materials and Methods: A total 150 patients with gallstone disease whose wall thickness of gallbladder were 3mm were selected for Laparoscopic cholecystectomy, Acute cholecystitis, choledocholithiasis, obstructive jaundice, Gallbladder mass or dilated CBD, were excluded. Details histroy, physical examination, and relevant investigation were done (including LFT, HbsAg, Anti HCV) and patients were checked for anesthesia fitness, then laparoscopic cholecystectomy were done, operative finding and outcome observed. Results: Among 150 patients there were 126patients were female and 24 patients were Male, age ranges from 16 to 65 yrs. Laparoscopic cholecystectomy were done successfully in 140 patients (92%) and conversion done in teen patients(6.6%). Major complications were seen in five (3.3%) patients, including common bile duct (CBD) injury in two patients, duodenal injury in one patient, bleeding from gall bladder bed in two patients. There was no mortality in the study. Conclusion: Laparoscopic cholecystectomy is a safe and effective treatment for gallstone disease. J. Dhaka National Med. Coll. Hos. 2019; 25 (02): 22-24
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".