The Prevalence of Substance Induced Psychosis & Substance Induced Mood Disorders in Adolescent Population
Bibliographic record
Abstract
Introduction: Drug and alcohol addiction is a leading cause of raising health care cost and has adverse effect on peoples health, social and occupational functioning. The purpose of this study was to investigate the prevalence of substance induced psychosis and mood disorders in adolescent population, and to determine what type of substance causing the presentation. Methods: Child psychiatry consults in the emergency department at Royal Alexandra Hospital, Edmonton, Canada during October, November, and December, 2007. Age was 12 to 17 years both sexes. Presenting complaints were either psychosis or mood symptoms. The diagnosis of substance induced disorder was made according to the DSM IV TR. Urine toxicology screen were obtained before discharge from Emergency department for cannabis, amphetamine, and cocaine. Blood test for ETOH was performed. Results: Total number of subjects was 27 patients. 70.37% presented with substance induced mood disorder, 29.62% with substance induced psychosis. For patients with psychosis (8, one was excluded, untested urine) 28.57% the urine test was positive for amphetamine, 42.85% marijuana, 14.28% cocaine, 14.28% negative test. For patients with mood disorder (19, 6 was excluded, untested urine) 76.92% urine was negative for substances, 15.30% blood was positive for ETOH, 7.69% urine was positive for cannabis. Discussion: In this preliminary data, a trend that substance induced mood disorder is more prevalent than substance induced psychosis. Substance is more to cause psychosis than mood disorder. Cannabis use is more than amphetamine and cocaine to produce psychosis. ETOH use is more than Cannabis to produce mood symptoms.
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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 | 0.001 |
| 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) | 0.003 | 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".