Near infrared indocyanine green fluorescent cholangiography versus intraoperative cholangiography to improve safety in laparoscopic cholecystectomy for gallstone disease-a systematic review protocol
Bibliographic record
Abstract
Background: Laparoscopic cholecystectomy has become the standard surgical approach in the treatment of cholelithiasis. Diverse surgical techniques and different imaging modalities have been described to evaluate the biliary anatomy and prevent or early detect bile duct injuries. X-ray intraoperative cholangiography (IOC) and near infrared indocyanine green fluorescent cholangiography (NIR-ICG) are safe and feasible techniques to assess biliary anatomy. The aim of this systematic review will be to evaluate if NIR-ICG can visualize extrahepatic biliary anatomy more efficiently and safer than IOC in minimally invasive cholecystectomy for gallstone disease.Methods: Literature search will be performed via MEDLINE (PubMed), Embase, Scopus, the Cochrane Central Register of Controlled Trials, and Web of Science Core Collection from 2009 to present. All randomized controlled clinical trials and prospective non-randomized controlled trials which report on comparison of NIR-ICG versus IOC will be included. All patients over 18 years old who require elective or urgent minimally invasive cholecystectomy (undergoing NIR-ICG during this procedure) due to gallstone disease both acute and chronic will be included. Since BDI has a low incidence, the primary outcome will be the ability to visualize extrahepatic biliary anatomy and the time to obtain relevant images of these structures.Two researchers will individually screen the identified records, according to a list of inclusion and exclusion criteria. Bias of the studies will be evaluated with the Newcastle-Ottawa score for non-randomized studies and with The Cochrane Risk of Bias Tool for randomized controlled trials. Quality of evidence for all outcomes will be determined with the GRADE system. The data will be registered in a predes
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.012 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".