Inflammatory markers and cognitive deficits in first-episode psychoses – A cross-sectional study
Bibliographic record
Abstract
Background: The first episode psychosis (FEP) is diagnosed when an individual exhibits signs of psychosis for the first time in a clinical setting. Various studies have observed elevated inflammatory markers such as C-reactive protein (CRP), Interleukin (IL)-6, IL-1B, Tumor Necrosis Factor (TNF) alpha, erythrocyte sedimentation rate (ESR), and neutrophil-lymphocyte ratio (NLR) in FEP. Cognitive deficits manifest early in patients with psychosis and contribute to poor clinical outcomes. Research indicates that cognitive deficits are linked to increased inflammatory biomarkers IL-6, IL1B, TNF-alpha, CRP). Aim: To evaluate the serum levels of inflammatory markers and severity of cognitive impairment in individuals with first-episode psychoses and to identify any potential associations between inflammatory markers and cognitive decline. Methods: Fifty drug-naïve patients with FEP visiting the department of Psychiatry at a tertiary care hospital were enrolled following permission from the Institutional Ethics Committee. After obtaining informed consent, they were interviewed using a semi-structured proforma and assessed using DSM-5. The Montreal Cognitive Assessment (MoCA) scale and Mini-Mental Status Examination (MMSE) assessed cognitive deficits. CRP, ESR, and NLR levels were measured, and the data collected were tabulated and statistically analyzed. Result: Out of 50 participants, 46% had elevated serum CRP levels, and 90% had elevated Neutrophil Lymphocyte Ratio levels well above clinical cut-offs. Most participants had normal ESR levels, and no vital association was seen between raised inflammatory markers and cognitive deficits. Conclusion: Patients with First Episode Psychosis had raised levels of inflammatory markers like CRP and NLR. This could be due to neuroinflammation, contributing to the development of psychosis. The study observed cognitive deficits in First Episode Psychosis to be poorly associated with inflammatory markers.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".