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Record W3043329125 · doi:10.1212/wnl.0000000000010370

Teaching NeuroImages: Reversible neuroimaging findings during treatment of infantile spasms with vigabatrin

2020· article· en· W3043329125 on OpenAlexaff
David Kim, Amit Sharma, Manas Sharma, Andrea Andrade

Bibliographic record

VenueNeurology · 2020
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsVigabatrinLevetiracetamAsymptomaticNeuroimagingMedicinePediatricsEpilepsyEpileptic spasmsMagnetic resonance imagingAnesthesiaSurgeryAnticonvulsantPsychiatryRadiology

Abstract

fetched live from OpenAlex

A 10-month-old boy with global developmental delay presented to clinic with a few months of infantile spasms occurring multiple times a day. His seizures continued despite vigabatrin (dosed at 133 mg/kg/d), levetiracetam, and steroid therapy. On vigabatrin, routine follow-up MRI showed abnormal signal change (figure), which may occur in 30.9% of patients.1 Risk is associated with a high peak dose but not cumulative.2 These findings are largely asymptomatic although rarely patients can present with hyperkinetic disorders.2 The imaging findings resolved on 4-month follow-up after tapering vigabatrin. At 18 months of age, the patient continues to have 1 seizure every 2 weeks.

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 score0.005
Threshold uncertainty score0.010

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 science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.280
Teacher spread0.258 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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