An Exploratory Study of Inflammatory Biomarkers and Extrapyramidal Side Effects in Patients with Schizophrenia
Bibliographic record
Abstract
Background: Extrapyramidal symptoms (EPS) are common side effects of antipsychotic medications and can contribute to medication non-adherence and subsequent relapse in schizophrenia. Recent research suggests that inflammatory markers, such as the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), and systemic immune-inflammation index (SII), may serve as biomarkers for treatment response in schizophrenia. This exploratory study is a first look at the relationship between these inflammatory markers and EPS in patients with schizophrenia. Study Design: Fifty patients with schizophrenia spectrum disorders were recruited, with 28 completing bloodwork. EPS was assessed using the Extrapyramidal Symptom Rating Scale (ESRS), and overall side effects were evaluated using the Glasgow Antipsychotic Side-Effect Scale (GASS). Blood samples were analyzed to calculate NLR, PLR, MLR, and SII. Study Results: Monocyte count was negatively correlated with the ESRS akathisia subscale. Higher SII scores were significantly associated with self-reported parkinsonism and hyperkinesia. Patients on clozapine had significantly higher PLR and MLR compared to those on other antipsychotics. No significant association was found between total GASS scores and inflammatory markers. Conclusions: These findings suggest that certain inflammatory markers may be associated with specific EPS subscales. Further research with larger samples is needed to validate these results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".