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Record W2614003428 · doi:10.1177/0883073817707300

Clinical and Radiologic Spectrum of Septo-optic Dysplasia: Review of 17 Cases

2017· article· en· W2614003428 on OpenAlexaff
Callie Alt, Michael Shevell, Chantal Poulin, Bernard Rosenblatt, Christine Saint‐Martin, Myriam Srour

Bibliographic record

VenueJournal of Child Neurology · 2017
Typearticle
Languageen
FieldMedicine
TopicFetal and Pediatric Neurological Disorders
Canadian institutionsMcGill University Health CentreMontreal Children's HospitalMcGill University
Fundersnot available
KeywordsSeptum pellucidumSchizencephalyPolymicrogyriaCorpus callosumOptic nerve hypoplasiaCortical dysplasiaDysplasiaDysgenesisMedicineHypoplasiaAutism spectrum disorderAgenesis of the corpus callosumMagnetic resonance imagingPathologyAnatomyRadiologyAutism

Abstract

fetched live from OpenAlex

We retrospectively reviewed the clinical and radiologic characteristics of 17 individuals with septo-optic dysplasia (SOD) and attempted to identify correlations between imaging findings, clinical features, and neurodevelopmental outcome. Surprisingly, only 1 (6%) individual was classified as classic SOD (with septum pellucidum/corpus callosum dysgenesis), 3 (18%) as SOD-like (with normal septum pellucidum/corpus callosum) and the majority, 13 (76%), as SOD-plus (with cortical brain malformation). Cortical abnormalities included schizencephaly, polymicrogyria, and gray matter heterotopias. All individuals had optic nerve hypoplasia, 11 (65%) had endocrinologic deficits, and 13 (76%) had abnormal cerebral midlines. Seven individuals (41%) had all 3 features. Neurodevelopmental outcome was abnormal in 13 (78%), ranging from mild to severe developmental delay. Individuals with SOD-plus did not have more severe neurologic deficits than individuals with classic or SOD-like subgroups. Thus, SOD is clinically and radiologically heterogeneous, and cortical abnormalities are very common. Neurodevelopmental deficits are very prevalent, and of wide-ranging severity.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.080
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.328
Teacher spread0.297 · 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

Citations53
Published2017
Admission routes1
Has abstractyes

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