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Record W126621080 · doi:10.1093/pch/9.3.166

Case 1: Teenager with seizures after snowboarding

2004· article· en· W126621080 on OpenAlexaff
Douglas Y. Mah, Sanjay Mahant

Bibliographic record

VenuePaediatrics & Child Health · 2004
Typearticle
Languageen
FieldMedicine
TopicInfectious Encephalopathies and Encephalitis
Canadian institutionsHospital for Sick ChildrenMcMaster University
Fundersnot available
KeywordsPhenytoinMedicineAnesthesiaNeurological examinationEmergency departmentGirlLumbar punctureLorazepamPhysical examinationSubclinical infectionStatus epilepticusEpilepsyCerebrospinal fluidPhenobarbitalPediatricsSurgeryInternal medicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

A 13-year-old girl arrived at a community emergency department following a fall while snowboarding. The patient had been unwell with a low-grade fever a week before this presentation. Six hours after her fall, she experienced four separate generalized tonic-clonic seizures, each lasting less than 3 min. She was treated with lorazepam and phenytoin. A lumbar puncture revealed zero red blood cells, 36 white blood cells with 94% lymphocytes. Cerebrospinal fluid (CSF) glucose and protein levels were normal. At the tertiary care centre, the patient had a fluctuating level of consciousness. Her vital signs were stable and she was afebrile. Her physical examination revealed a drowsy girl with a generally normal examination. Her muscle tone was reduced but symmetrical. There were no focal neurological findings. Acyclovir and ceftriaxone were started empirically. Phenobarbital and phenytoin were given for seizure management. Computed tomography and magnetic resonance imaging of the head were normal. CSF cultures were negative. The patient continued to have seizures despite treatment. Continuous electroencephalogram monitoring showed obvious seizure activity, including many subclinical episodes. Multiple antiepileptic drugs were required to achieve adequate seizure control. Further investigation revealed the cause of this girl's problem.

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 score0.332
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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