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Record W1987880890 · doi:10.1177/08830738060210021001

Infantile Spasms as an Adverse Outcome of Neonatal Cortical Sinovenous Thrombosis

2006· article· en· W1987880890 on OpenAlexaff
Teesta Soman, Mahendranath Moharir, Gabrielle deVeber, Shelly K. Weiss

Bibliographic record

VenueJournal of Child Neurology · 2006
Typearticle
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineLethargyThrombosisPediatricsEpilepsySuperior sagittal sinusVenous thrombosisSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Cerebral sinovenous thrombosis is a rare but potentially serious condition often occurring in children with nonspecific presenting features. Much remains to be learned about the long-term outcome of infants with cerebral sinovenous thrombosis. We report a series of four patients taken from a prospective database of neonates with sinovenous thrombosis who subsequently developed infantile spasms, three with hypsarrythmia on electroencephalography and one with multiple independent spike foci. The first patient presented at 2 weeks of age with hypernatremia, dehydration, and seizures. He was found to have extensive thrombosis and hemorrhagic infarction of the right basal ganglia. The second patient presented at 5 weeks of life and was found to have sagittal sinus thrombosis with bilateral intracranial hemorrhage. The third patient presented with seizures on day 1 of life and was found to have venous thrombosis involving the torcular, extending into the sagittal sinus. The fourth patient presented at 3 weeks with lethargy and seizures. He was diagnosed with bacterial meningitis and also had extensive sinus thrombosis. All patients developed infantile spasms at ages 9, 7, 11, and 10 months, respectively. This is the first report in the English literature describing infantile spasms as a possible outcome of sinovenous thrombosis in early infancy.

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 score0.011
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.013
GPT teacher head0.280
Teacher spread0.267 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

Explore more

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