The Role of CT cholangiography in the Detection and Localisation of Suspected Bile Leakage Following Cholecystectomy
Bibliographic record
Abstract
BACKGROUND: Most bile duct injuries are not recognized at the time of initial surgery. Optimal treatment requires early recognition. CT IVC has become increasingly important in identifying bile leaks and their source after cholecystectomy. Our study aims to report the outcomes of using CT IVC post operatively and how accurately it can detect or localise bile leaks. METHODS: From 2000 - 2009, twenty patients were managed for suspected bile leak post cholecystectomy within the Alfred Hospital. The study included a retrospective evaluation of the initial procedure, presenting symptoms, site of ductal injury, diagnostic procedures and therapeutic interventions. Results were analysed to determine success of the imaging procedure, and to correlate imaging diagnosis with results both diagnostically and clinically. RESULTS: Twenty patients had a suspected bile leak, of which 3 were detected at the time of surgery. Seven patients had a CTIVC as their primary investigation. It identified bile leak in 6 and the anatomical site in 5. One had a leak excluded and was managed conservatively. CONCLUSIONS: CT Cholangiography is a feasible and low-risk tool for imaging of the biliary tract in suspected bile leaks post cholecystectomy. It is a valuable non-invasive investigation that may help avoid endoscopic retrograde Cholangiography or 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".