Cognitive impairment and elevated neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio in schizophrenia
Bibliographic record
Abstract
OBJECTIVES: The efficacy of current schizophrenia treatments on cognitive symptoms remains limited owing to constrained data around the aetiopathogenesis of such symptoms. Complete blood cell counts have been used to identify variations in inflammatory responses among individuals with schizophrenia. This study aimed to determine associations between inflammatory markers and cognitive impairment in patients with schizophrenia. METHODS: Patients with schizophrenia aged 20 to 40 years were recruited from Prof Dr M Ildrem Hospital, Medan, Indonesia. Diagnoses were made by psychiatrists based on the DSM-5 criteria. Healthy controls matched for age, sex, and body mass index were recruited from the local community. The severity of schizophrenia was assessed by a psychiatrist using the Positive and Negative Syndrome Scale. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA). Complete blood cell counts were performed. Absolute neutrophil, lymphocyte, monocyte, and white blood cell (WBC) counts were quantified, and the neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) were calculated. RESULTS: = -0.351, p = 0.011) were negatively correlated with MoCA scores. CONCLUSION: Patients with schizophrenia show signs of cognitive impairment and elevated WBC counts, neutrophil counts, and NLR. These peripheral inflammatory markers may be used to enhance understanding of the complex inflammatory theory of psychotic disorders.
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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".