Mini-laparoscopy in the endoscopy unit
Bibliographic record
Abstract
PURPOSE OF REVIEW: The evaluation of liver histology is an important component of the diagnosis and staging of liver diseases. The most common technique employed to sample liver tissue for decades has been percutaneous liver biopsy. Although this is a relatively well tolerated technique in the early stages of liver disease, it carries a high risk of complications, particularly hemorrhage, in patients with advanced cirrhosis. Mini-laparoscopy allows macroscopic assessment and biopsy under direct vision and therefore is a well tolerated and effective technique. RECENT FINDINGS: The major advantages of this technique are direct visualization of the liver surface, thereby allowing inspection for morphologic changes of cirrhosis as well as targeted biopsies, the ability to immediately treat potential complications (bleeding and bile leakage), furthermore the peritoneal cavity can be visualized to stage gastrointestinal (GI) malignancies. Additionally, 'blind' percutaneous liver biopsy fails to establish a diagnosis in about 25% of cases, largely because of sampling error. SUMMARY: This technique presents the opportunity to visualize the surface of the liver and the peritoneal cavity, making it a valuable tool for liver biopsy. This review summarizes the technique of mini-laparoscopy and addresses its potential uses and limitations as a diagnostic modality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".