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Record W4385851425 · doi:10.1002/ajmg.b.32956

Psychiatric and neurological manifestations in adults with Smith–Magenis syndrome: A scoping review

2023· review· en· W4385851425 on OpenAlexaff
Dorinde Korteling, Jiska L. I. Musch, Janneke Zinkstok, Erik Boot

Bibliographic record

VenueAmerican Journal of Medical Genetics Part B Neuropsychiatric Genetics · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsUniversity Health Network
FundersUniversiteit Utrecht
KeywordsPsychiatryIntellectual disabilityAutismAutism spectrum disorderNeurodevelopmental disorderPsychologyPervasive developmental disorderAttention deficit hyperactivity disorderMedicineClinical psychology

Abstract

fetched live from OpenAlex

Smith-Magenis syndrome (SMS) is a neurodevelopmental disorder caused by a 17p11.2 deletion or a pathogenic variant of the RAI1 gene, which lies within the 17p11.2 region. Various psychiatric and neurological disorders have been reported in SMS, with most literature focusing on children and adolescents. To provide an overview of the current knowledge on this topic in adults with SMS, we performed a comprehensive scoping review of the relevant literature. Our findings suggest that many manifestations that are common in childhood persist into adulthood. Neuropsychiatric manifestations in adults with SMS include intellectual disability, autism spectrum- and attention deficit hyperactivity disorder-related features, self-injurious and physical aggressive behaviors, sleep-wake disorders, and seizures. Findings of this review may facilitate optimization of management strategies in adults with SMS, and may guide future studies exploring late-onset psychiatric and neurological comorbidities in SMS.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.310
Teacher spread0.291 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Medical Genetics Part B Neuropsychiatric GeneticsSame topicGenomic variations and chromosomal abnormalitiesFrench-language works237,207