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Record W4377940720 · doi:10.1097/ypg.0000000000000343

White matter volume and myelin oligodendrocyte glycoprotein (MOG) microsatellites in pediatric obsessive-compulsive disorder

2023· article· en· W4377940720 on OpenAlexafffund
Gwyneth Zai, Clement C. Zai, Paul Arnold, Margaret A. Richter, Gregory L. Hanna, David Rosenberg, James L. Kennedy

Bibliographic record

VenuePsychiatric Genetics · 2023
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsSunnybrook Health Science CentreHotchkiss Brain InstituteHospital for Sick ChildrenUniversity of CalgaryUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of Mental HealthCanadian Institutes of Health ResearchAlberta InnovatesHospital for Sick ChildrenUniversity of TorontoNational Institutes of HealthWayne State University
KeywordsWhite matterMyelin oligodendrocyte glycoproteinMedicineBrain sizeMyelinOligodendrocytePathologyMagnetic resonance imagingInternal medicineCentral nervous systemRadiology

Abstract

fetched live from OpenAlex

The myelin oligodendrocyte glycoprotein ( MOG ) gene plays an important role in myelination and has been implicated in the genetics of white matter changes in obsessive-compulsive disorder (OCD). We examined the association between variations of two microsatellite markers across MOG for association and total white matter volume as measured using volumetric MRI in 37 pediatric OCD patients 7-18 years. We compared white matter volumes between microsatellite allele groups using analysis of covariance with covariates of age, gender, and total intracranial volume. After controlling for multiple comparisons, a significant relationship was detected between MOG (TAAA)n and increased total white matter volume ( P = 0.018-0.028). Although preliminary, our findings provide further support for the involvement of MOG in OCD.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.260
Teacher spread0.252 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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