Inflammatory alterations in dual diagnosis patients with schizophrenia and substance use disorders
Bibliographic record
Abstract
The lifetime prevalence of substance use disorders (SUDs) is high (~50%) in schizophrenia (SCZ). The study objective was to evaluate the inflammatory status of dual diagnosis (DD) patients with SCZ and SUDs. Patients (n = 29) were diagnosed with a schizophrenia‐spectrum disorder and a comorbid SUD (cannabis>alcohol>cocaine) (DSM‐IV criteria). Psychiatric symptoms were evaluated with the Positive and Negative Syndrome Scale and the Calgary Depression Scale for Schizophrenia. Healthy controls (n = 28) consisted of sex‐ and age‐matched volunteers without any known history of SCZ or SUDs. Plasma cytokines were assessed by multiplex immunoassay or by monoplex ELISA for higher sensitivity. Between‐group differences were assessed using ANOVA, and level of significance was set at p < 0.05. Plasma levels of interleukin (IL)‐6, IL‐8, IL‐1 receptor antagonist, and soluble IL‐2 receptor were increased in DD patients, compared to controls, while L‐17 was normal. Both IL‐6 and IL‐8 were associated with the severity of depressive symptoms and with alcohol consumption. These findings indicate that an inflammatory syndrome does exist in DD patients, and it may play a role in psychopathology. Future studies will elucidate the contribution of specific drugs of abuse and their interactions with SCZ in the dysregulation of inflammatory cytokines in DD patients. Supported by CIHR, and by AstraZeneca Pharmaceuticals.
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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 | 0.000 |
| 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".