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Record W2328791458 · doi:10.1097/pec.0000000000000388

Vertebral Artery Dissection Causing Stroke After Trampoline Use

2015· article· en· W2328791458 on OpenAlexaff
Courtney Casserly, Rodrick Lim, Asuri N. Prasad

Bibliographic record

VenuePediatric Emergency Care · 2015
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineTrampolineVertebral artery dissectionStroke (engine)Dissection (medical)Magnetic resonance imagingVertebral arteryEmergency departmentRadiologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to report a case of a 4-year-old boy who had been playing on the trampoline and presented to the emergency department (ED) with vomiting and ataxia, and had a vertebral artery dissection with subsequent posterior circulation infarcts. METHODS: This study is a chart review. RESULTS: The patient presented to the emergency department with a 4-day history of vomiting and gait unsteadiness. A computed tomography scan of his head revealed multiple left cerebellar infarcts. Subsequent magnetic resonance imaging/magnetic resonance angiogram of his head and neck demonstrated multiple infarcts involving the left cerebellum, bilateral thalami, and left occipital lobe. A computed tomography angiogram confirmed the presence of a left vertebral artery dissection. CONCLUSIONS: Vertebral artery dissection is a relatively common cause of stroke in the pediatric age group. Trampoline use has been associated with significant risk of injury to the head and neck. Patients who are small and/or young are most at risk. In this case, minor trauma secondary to trampoline use could be a possible mechanism for vertebral artery dissection and subsequent strokes. The association in this case warrants careful consideration because trampoline use could pose a significant risk to pediatric users.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.582

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 integrity0.0000.000
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 head0.030
GPT teacher head0.277
Teacher spread0.247 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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