Subcapsular Hepatic Hematoma Post-Endoscopic Retrograde Cholangiopancreatography Requiring Surgical Necrosectomy
Bibliographic record
Abstract
Cholelithiasis is a common gastrointestinal pathology with a prevalence of over 6% in the USA. Symptomatic patients can develop cholangitis, biliary colic, pancreatitis and cholecystitis. Surgical management involves laparoscopic or open cholecystectomy. Stones within the common bile duct can be treated with endoscopic retrograde cholangiopancreatography (ERCP). Well-known ERCP complications include pancreatitis, perforation, bleeding and cholangitis. Hepatic hematomas as a complication of ERCP are extremely rare, with fewer than 50 reported cases in the literature. Approximately 22% have required operative management. We present an extremely rare case of ERCP-associated subcapsular hepatic hematoma in a 43-year-old lady that was initially non-operatively managed. She did not improve with antibiotics alone and underwent attempted interventional radiology drainage. Despite this, due to on-going sepsis, the patient underwent laparoscopic necrosectomy and drain placement with continued post-operative irrigation. After a long course of antibiotics and drain irrigation, the patient was discharged with repeated computed tomography imaging showing almost total resolution of the infected collection. This case highlights the extreme rarity of surgical management for post-ERCP subcapsular hepatic hematoma and its successful outcome.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".