Peripheral sub-inflammation is associated with antidepressant consumption in schizophrenia. Results from the multi-center FACE-SZ dataset
Bibliographic record
Abstract
Objectives The relation between C-reactive protein (CRP), depression and antidepressant consumption has been well explored in major depressive disorders but not in schizophrenia, which has a high rate of depression comorbidity. The objectives of this study were: – to determine the prevalence of abnormal CRP levels, depression and antidepressant consumption in a multi-center community-dwelling sample of subjects with schizophrenia; – to determine the association between abnormal CRP levels, depression and antidepressant consumption in schizophrenia. Method Two hundred and nineteen stable patients with schizophrenia (mean age = 31.6 years, 75.3% male gender) were systematically included in the multicentre network of FondaMental Expert Center for schizophrenia (FACE-SZ) and assessed with Calgary Depression Scale for depression. High sensitivity CRP (hs-CRP) was measured with an assay using nephelometry (Dade Behring). Abnormal CRP level was defined by levels > 3 mg/L. Current medication was recorded. Results Overall, 63 subjects (28.8%) were found to have abnormal CRP levels, 43 (20.1%) received a diagnosis of comorbid current depression, and 51 (31.9%) had ongoing antidepressant treatment. In univariate analysis, abnormal CRP levels were found to be significantly associated with metabolic syndrome (P = 0.0011) and with antidepressant consumption (P = 0.01), while depression, psychotic symptomatology, age of onset, illness duration, sociodemographic characteristics, current tobacco or cannabis status were not (all P > 0.05). In a multivariate model, abnormal CRP was highly associated with antidepressant consumption independently of other confounding variables (adjusted odd ratio = 2.9, 95% confidence interval 1.2–6.8). Conclusion Abnormal CRP levels in schizophrenia were found to be associated with antidepressant consumption, but not with depression. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".