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Record W188269916 · doi:10.1177/070674370104601004

Childhood-Onset Schizophrenia: Research Update

2001· review· en· W188269916 on OpenAlexaffvenue
Sanjiv Kumra, Maryanne Shaw, Patricia Merka, Emilie Y. Nakayama, Rachel Augustin

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2001
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)Age of onsetPsychosisPsychiatryPopulationPsychologyDiseaseVulnerability (computing)Clinical psychologyMedicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

This review is a research update of recent literature related to childhood-onset schizophrenia (onset of psychotic symptoms by age 12 years). This subgroup of patients has attracted considerable research interest because patients with a childhood onset may represent a more homogeneous patient population in which to search for risk or etiologic factors. We examine data indicating that childhood-onset schizophrenia (COS) shares the same clinical and neurobiologic features as later-onset forms of the disorder. Compared with adults with schizophrenia, however, this subgroup of patients appears to have more severe premorbid neuro-developmental abnormalities, more cytogenetic anomalies, and potentially greater family histories of schizophrenia and associated spectrum disorders. While preliminary, these data indicate that a greater genetic vulnerability may be one of the underpinnings of COS. Future studies of this subgroup may provide important clues as to the genetic basis for schizophrenia and how gene products influence certain features of the disease, such as age of onset and mode of inheritance.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.050
GPT teacher head0.370
Teacher spread0.320 · 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 designNot applicable
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

Citations47
Published2001
Admission routes2
Has abstractyes

Explore more

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