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Record W2052867103 · doi:10.1097/mog.0b013e3283561f25

Mini-laparoscopy in the endoscopy unit

2012· review· en· W2052867103 on OpenAlexaff
Arthur Hoffman, Farial Naima Rahman, Sanjay K. Murthy, Peter R. Galle, Ralf Kießlich

Bibliographic record

VenueCurrent Opinion in Gastroenterology · 2012
Typereview
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCirrhosisLaparoscopyLiver biopsyRadiologyBiopsyEndoscopyLiver diseaseHepatocellular carcinomaPercutaneousStage (stratigraphy)GastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.308
GPT teacher head0.409
Teacher spread0.101 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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