Dual diagnosis, the effects of substance abuse on patients with schizophrenia
Bibliographic record
Abstract
Comorbidity between Axis I mental disorders and substance use disorders range from 5%--60% (Farrell, 1998; Fowler, 1998). It has been suggested that dually diagnosed patients are inadequately treated for both disorders and that they are problematic from a diagnostic, clinical management and economic perspective. Dual Diagnosis (DD) maybe associated with a number of issues including increased aggression, increased non-compliance with medication (Swartz, 1998), and exacerbated psychopathology (Tomasson, 1997). However, contradictory evidence has also been found (Leon, 1998), which suggests that patients with DD may be a higher functioning population of mentally ill patients. The objectives of the present study were to determine the prevalence and clinical characteristics of dual diagnosis patients in a chronic psychiatric population. A sample of 217 patients with schizophrenia spectrum disorders was randomly sampled from the psychiatric facilities of the Montreal General Hospital. Almost half of the sample presented with comorbid addictive disorders; the most common drugs abused were alcohol, cannabis and cocaine. Those patients who had a lifetime diagnosis of substance abuse or dependence were more likely to be male, had a more severe course of psychiatric illness, higher rates of psychiatric symptomology, were more likely to be tobacco smokers and had higher rates of non-compliance with psychiatric medications. Further analyses revealed lower levels of social support and more legal problems in patients with DD, all of which may negatively impact on the quality of care for dual diagnosis patients in the clinical setting.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".