MétaCan
Menu
Back to cohort
Record W2137360930 · doi:10.3174/ajnr.a1853

In Vivo Experimental Intracranial Aneurysm Models: A Systematic Review

2009· review· en· W2137360930 on OpenAlexaff
Fatiha Bouzeghrane, Olivier Naggara, David F. Kallmes, Alejandro Berenstein, Jean Raymond

Bibliographic record

VenueAmerican Journal of Neuroradiology · 2009
Typereview
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsCentre Hospitalier de l’Université de MontréalUniversité de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsMedicineAneurysmRadiologyAnimal modelMedical physicsOcclusionPreclinical researchSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Animal models are necessary to develop and test innovations in aneurysm therapy before clinical introduction. This review aims at identifying the most likely candidates for standardizing preclinical testing of aneurysm devices. We systematically searched electronic databases for publications on animal aneurysm models from 1961-2008 to assess the methodologic quality of the studies and collect data on the patency and angiographic and pathologic outcomes of treatments. There has been a steady increase in the annual number of publications with time. Species that were most frequently used were dogs, rabbits, and rodents, followed by swine. Most publications are single-laboratory studies with variables and poorly validated outcome measures, a small number of subjects, and limited standardization of techniques. The most appropriate models to test for recurrences after endovascular occlusion were the surgical bifurcation model in dogs, and the elastase-induced aneurysm model in rabbits. A standardized multicenter study is needed to improve the preclinical evaluation of endovascular devices in aneurysm therapy.

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)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0070.008
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.338
Teacher spread0.300 · 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 designSystematic review
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

Citations95
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of NeuroradiologySame topicIntracranial Aneurysms: Treatment and ComplicationsFrench-language works237,207