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Record W1540607895 · doi:10.1155/2006/743783

Emergency Management of Bleeding Esophageal Varices: Drugs, Bands or Sleep?

2006· review· en· W1540607895 on OpenAlexaffvenue
Brian Yan, Samuel S. Lee

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2006
Typereview
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTerlipressinMedicineBalloon tamponadeOctreotideEsophageal varicesSclerotherapyEndoscopyDesmopressinIntensive care medicineSurgeryPortal hypertensionCirrhosisInternal medicineSomatostatin

Abstract

fetched live from OpenAlex

Variceal bleeding is a severe complication of cirrhosis leading to significant morbidity and mortality. Treatment of acute variceal bleeding has improved dramatically since the era of the mechanical balloon tamponade. These advances include endoscopic band ligation or sclerotherapy, and vasoactive pharmacological options such as somatostatin, octreotide, vasopressin and terlipressin. Evidence from a multitude of clinical trials and meta-analyses comparing endoscopic and pharmacological treatments suggests near equivalence in efficacy for initial hemostasis, mortality and rate of rebleeding. This raises the question of whether on-call gastroenterologists should be performing emergency endoscopic treatment in the middle of the night or start pharmacological treatment and delay endoscopy until optimal patient and working conditions the next morning. The present review analyzes the available comparative data between endoscopic and pharmacological treatment options. Although the literature cannot yet definitively answer the question posed, the authors suggest that delaying endoscopic treatment until the next morning may be the most reasonable practical approach.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.026
GPT teacher head0.290
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations29
Published2006
Admission routes2
Has abstractyes

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