Population Growth Rates of Psychosis and Effective Interventions
Bibliographic record
Abstract
Limited research is available on children and adolescent psychosis. Thus, we conducted a Meta-Analysis of pharmaceutical treatments, using literature from the last 5 years. A 16 year cumulative prevalence of psychosis in the Calgary Health Region was also examined. Conduct a Meta-Analysis to determine effective treatments for children and adolescent psychosis. Conduct a prevalence study for the Calgary Health Zone from 1994-2009. Update and identify effective treatment interventions for children and adolescent psychosis, and determine psychosis prevalence in the Calgary Health Region. Direct physician billing data was used for the Calgary Health Region (Alberta) from 1994-2009 (n = 763449) to identify 73078 unique individuals (30762-males, 42316-females), each of whom had a physician-assigned diagnosis of psychosis. Using standard methods, 41 studies were identified and those meeting inclusion criteria were compared and ranked on the basis of effect size, study design, etc. across studies. The 16 year cumulative prevalence of psychosis per 10,000 was 1026 for all ages, and 229 under 19. 4 studies met measurable outcomes; 34 results were positive, 17 were equivalent, and no results were negative. The greatest effect size was 1.03, while the lowest was −0.286. Risperidone, Quetiapine, and Olanzapine were effective treatments. Risperidone, Quetiapine, and Olanzapine are effective treatments for children and adolescent psychosis. Psychosis rates increased 2.3 times for all ages, and 2 times under age 19, from 1994-2009. Psychosis in the under 19 male population is becoming more prevalent and increasing at a higher rate compared to females.
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 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 | 0.056 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.032 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".