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Record W2965938158 · doi:10.1177/2329048x19867800

Lateral Medullary Syndrome Due to Left Vertebral Artery Occlusion in a Boy Postflexion Neck Injury

2019· article· en· W2965938158 on OpenAlexaff
Abdulla Alawadhi, Christine Saint‐Martin, Christine Sabapathy, Guillaume Sébire, Michael Shevell

Bibliographic record

VenueChild Neurology Open · 2019
Typearticle
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsMedicineVertebral arteryMagnetic resonance imagingMedullary cavityMagnetic resonance angiographyRadiologyContext (archaeology)MedullaOcclusionPopulationAnatomySurgery

Abstract

fetched live from OpenAlex

Lateral medullary syndrome is rare in pediatrics. It is characterized by neurological deficits due to an ischemic lesion in the lateral medulla. The authors describe a 17-year-old boy who developed lateral medullary syndrome in the context of a hyperflexion neck injury while diving in shallow water with traumatic vascular injury. He had "crossed" neurological deficits above and below the neck. His magnetic resonance angiography showed intra- and extracranial left vertebral artery occlusion and his magnetic resonance imaging showed signal abnormality involving the left lateral medulla and inferomedial cerebellum in keeping with an infarct secondary to left vertebral artery and left posterior inferior cerebellar artery occlusion. Good neurological recovery was observed on heparin therapy started after surgical treatment of traumatic injury. To our knowledge, this is the first reported case of lateral medullary syndrome in a pediatric population related to a flexion neck injury. The authors emphasize the importance of a high level of suspicion for accurate diagnosis.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.261
Teacher spread0.252 · 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 designCase report
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

Citations3
Published2019
Admission routes1
Has abstractyes

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