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Record W2166816606 · doi:10.1177/0883073811408904

Intracranial Dissection Mimicking Transient Cerebral Arteriopathy in Childhood Arterial Ischemic Stroke

2011· article· en· W2166816606 on OpenAlexaff
Nomazulu Dlamini, Jeremy L. Freeman, Mark T. Mackay, Cynthia Hawkins, Manohar Shroff, Heather J. Fullerton, Gabrielle deVeber

Bibliographic record

VenueJournal of Child Neurology · 2011
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineCarotid artery dissectionMagnetic resonance angiographyArterial dissectionRadiologyMagnetic resonance imagingStroke (engine)Dissection (medical)Cerebral angiographyAngiographyNeuroradiologyCardiologyNeurology

Abstract

fetched live from OpenAlex

Transient cerebral arteriopathy is a frequent cause of childhood arterial ischemic stroke. Differentiating this condition from intracranial carotid artery dissection is challenging but important for initial treatment. We describe 4 cases from the International Pediatric Stroke Study of intracranial carotid artery dissection, initially misdiagnosed as transient cerebral arteriopathy. Presentations were abrupt, with focal neurological deficits in 4, preceding headache in 3, and minor trauma in 1. Infarcts involved the anterior circulation, and magnetic resonance angiography showed unilateral arterial stenosis/occlusion. None had evidence of dissection. All received anticoagulation or thrombolysis. Three died from refractory intracranial hypertension. Intracranial carotid artery dissection was confirmed postmortem (n = 3) and on dedicated MR wall imaging showing intramural hematoma (n = 1). In differentiating transient cerebral arteriopathy from intracranial carotid artery dissection, routine magnetic resonance angiography is unreliable and adjunctive conventional angiography, gadolinium magnetic resonance angiography, or dedicated MRI wall imaging should be considered.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations61
Published2011
Admission routes1
Has abstractyes

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