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Record W1967597394 · doi:10.1080/21642850.2014.927738

Childhood-onset schizophrenia: what do we really know?

2014· article· en· W1967597394 on OpenAlexaff
Jennifer Bartlett

Bibliographic record

VenueHealth Psychology and Behavioral Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyAnhedoniaSchizophrenia (object-oriented programming)PsychiatryClinical psychologySuicidal ideationAffect (linguistics)AggressionEtiologyPsychosisMedicinePoison controlInjury prevention

Abstract

fetched live from OpenAlex

Childhood-onset schizophrenia (COS) is a rare, chronic mental illness that is diagnosed in children prior to the age of 13. COS is a controversial diagnosis among clinicians and can be very difficult to diagnose for a number of reasons. Schizophrenia is a psychotic disorder characterized by hallucinations, delusions, flat affect, limited motivation and anhedonia. The psychotic nature of this disorder is quite disruptive to the child's emotional regulation, behavioural control and can reduce the child's ability to perform daily tasks that are crucial to adaptive functioning. Prior to the onset of schizophrenia, children often develop premorbid abnormalities, which are disturbances to a child's functioning that may serve as warning signs. These disturbances can manifest in a variety of behavioural ways and may include introversion, depression, aggression, suicidal ideation and manic-like behaviours. This article will review the clinical presentation of schizophrenia in children and examine the existing knowledge around aetiology, treatment approaches, assessment techniques and differential diagnostic considerations. Gaps in the literature are identified and directions for future research are discussed.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.007
Open science0.0010.001
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.428
Teacher spread0.374 · 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

Citations27
Published2014
Admission routes1
Has abstractyes

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