Factors Differentiating Childhood-Onset and Adolescent-Onset Schizophrenia
Bibliographic record
Abstract
BACKGROUND: The greater severity and burden of illness in individuals with early onset schizophrenia (ie, before age 18 years) deserves further investigation, specifically regarding its prevalence in community-based treatment and its association with other psychiatric or medical conditions. METHOD: A retrospective cohort design was employed using the South Carolina Medicaid claims database covering outpatient and inpatient medical services from January 1, 1999, through December 31, 2013, to identify patients aged ≤ 17 years with a diagnosis of schizophrenia spectrum disorders (ICD-9-CM). Logistic regression was used to examine the factors differentiating childhood- versus adolescent-onset schizophrenia in a community-based system of care. RESULTS: Early onset schizophrenia was diagnosed in 613 child and adolescent cases during the study epoch or 0.2% of this population-based cohort. The early onset cohort was primarily male (64%) and black (48%). The mean length of time followed in the Medicaid dataset was 12.6 years. Within the early onset cohort, 22.5% were diagnosed at age ≤ 12 years and 77.5% were diagnosed as adolescents. The childhood-onset subgroup was twice as likely to have speech, language, or educational disabilities and an attention-deficit/hyperactivity disorder diagnosis but significantly less likely to have schizophrenia or schizoaffective disorder, an organic brain disorder or mental retardation/intellectual disability, or a substance use disorder (adjusted OR = 2.01, 2.26, 0.38, 0.31, 0.47, and 0.32, respectively) compared to the adolescent-onset subgroup. CONCLUSION: Primary care providers should identify and maintain surveillance of cases of pediatric neurodevelopmental disorders, which appear to be highly comorbid and genetically related, and refer them early and promptly for specialized treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".