Oxytocin levels in individuals with schizophrenia are high in cerebrospinal fluid but low in serum: A systematic review and meta-analysis
Bibliographic record
Abstract
Schizophrenia is a debilitating mental illness. Levels of oxytocin have been proposed as a biomarker of schizophrenia; however, the observed levels of oxytocin in individuals with schizophrenia have been inconsistent across studies. We performed a meta-analysis to evaluate oxytocin levels in plasma, serum and cerebrospinal fluid to see if there are statistically different concentrations between individuals with schizophrenia and the comparison group. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Following the inclusion and exclusion criteria, 14 studies were included in the meta-analysis. The quality of the study was evaluated by the Newcastle-Ottawa Scale (NOS). A random-effects model was performed using the Comprehensive Meta-analysis software with the standardized mean difference (SMD) and 95% confidence intervals (CIs). Serum oxytocin levels in individuals with schizophrenia were significantly lower than that in comparison group (SMD = - 1.74, 95% CI = - 3.22 to - 0.26, p = 0.02) but cerebrospinal fluid oxytocin levels in individuals with schizophrenia were significantly higher than those in the comparison group (SMD = 0.55, 95% CI = 0.05 to 1.04, p = 0.03). Our results suggest that oxytocin levels in cerebrospinal fluid are increased in individuals with schizophrenia but decreased in serum. Therefore, the oxytocin system dysregulation may play a role in the pathophysiology of schizophrenia and it should be measured in more populations for a possible implementation as a biomarker of schizophrenia.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".