Fluorescent Cholangiography in Laparoscopic Cholecystectomy: An Updated Canadian Experience
Bibliographic record
Abstract
Background. Laparoscopic cholecystectomy (LC) is one of the most common general surgery procedures in Canada with approximately 100 000 cases performed per year. Bile duct injury remains a morbid complication with an incidence rate of 0.3% to 0.5%. Indocyanine green (ICG) fluorescent cholangiography is a noninvasive technology aiding in real-time identification of biliary structures for safe dissection within Calot’s triangle. The objectives were to provide an update to our initial experience with ICG aiding in the identification of biliary structures and ensuring that no adverse patient reactions occurred with ICG administration. Methods. Prospective case series from 2016 to 2018 for elective LC with ICG technology performed at a single academic teaching institution. Patient demographics, indications for operation, biliary structures visualized, amount of ICG used, operative times, and complications were recorded. Results. One hundred eight cases were included for review. The cystic duct, common hepatic duct, and common bile duct were identified with ICG in 90%, 48%, and 84% of cases, respectively. ICG simultaneously visualized at least 2 of 3 biliary structures 83.4% of the time. Only 1 biliary structure was identified in 10% of cases. No biliary structures were identified in 6% of cases. Mean initial ICG dose given was 1.65 mL. No adverse patient reactions to ICG were noted. Conclusions. This updated series illustrates that administration of ICG enhances visualization of the biliary system during outpatient LC. ICG is safe and its application should be further studied in early LC for acute cholecystitis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".