Endoscopic ultrasonography drainage and debridement of an infected subcapsular hepatic hematoma: A case report
Bibliographic record
Abstract
BACKGROUND: Endoscopic ultrasonography (EUS) has evolved in the last years making it not only a diagnostic modality but a therapeutic procedure. EUS is now used as an alternative technique to percutaneous and surgical drainage. Even though EUS is a challenging procedure and not always suitable compared to percutaneous drainage, there is a need for developing new therapeutic approaches to the liver for when percutaneous drainage is not feasible. CASE SUMMARY: We present the case of a 82 years old male who developed an infected subcapsular hepatic hematoma (SHH) of the left lobe following percutaneous biliary drainage. After 2 failed attempts of percutaneous drainage of the SHH and because the patients couldn't withstand surgery, we conducted a EUS drainage and debridement of the SHH. Using a lumen apposing metal stent (LAMS) by a transgastric approach, we were able to gain endoscopic access to the SHH. With our experience in the debridement of walled off pancreatic necrosis using this technique, we were confident it was the right approach. After four debridement sessions, the computed tomography scan showed a clear regression of the SHH. CONCLUSION: To our knowledge, this is the first case of successful endoscopic debridement of a SHH using a LAMS which appear to be feasible and safe in this specific case.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".