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Record W1969652232 · doi:10.1177/0883073811420495

The Demographic, Clinical, and Magnetic Resonance Imaging (MRI) Features of Transverse Myelitis in Children

2011· article· en· W1969652232 on OpenAlexafffund
Terrence Thomas, Helen M. Branson, Leonard H. Verhey, Manohar Shroff, Derek Stephens, Sandra Magalhaes, Brenda Banwell

Bibliographic record

VenueJournal of Child Neurology · 2011
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick ChildrenMultiple Sclerosis Scientific Research Foundation
KeywordsTransverse myelitisNeuromyelitis opticaMedicineMagnetic resonance imagingMultiple sclerosisNeuroimagingMyelitisAcute disseminated encephalomyelitisCerebrospinal fluidPathologyPediatricsRadiologySpinal cordPsychiatry

Abstract

fetched live from OpenAlex

The authors collected demographic, clinical, and neuroimaging data prospectively on 38 children with transverse myelitis. One child died during the illness. The female:male ratio was 1.2:1 for children under age 10 years and 2.6:1 over age 10 years. Twenty-eight (74%) reported a prodromal event. Twenty-two patients (58%) had longitudinally extensive transverse myelitis, 9 (24%) had focal lesions, and 5 (13%) had both. Twenty of 33 with brain imaging (61%) had brain lesions; 7 fulfilled McDonald criteria for dissemination in space. Seven of 22 (36%) tested had cerebrospinal fluid oligoclonal banding, 6 of whom had brain lesions. Serum neuromyelitis optica IgG antibodies were absent in all 20 of the children for whom this test was available. At follow-up (mean 3.2 ± 2.0 years), 16% are wheelchair-dependent, 22% have persisting bladder dysfunction, and 13% have been diagnosed with multiple sclerosis.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.296
Teacher spread0.273 · 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 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

Citations83
Published2011
Admission routes2
Has abstractyes

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