CRP and its relation to cognitive performance in schizophrenia patients: a cross-sectional study
Bibliographic record
Abstract
Background Schizophrenia (SZ) is one of the most severe and chronic forms of mental illness. It involves cognition, emotion, perception, and behavior. There is an obvious role of neuroinflammation and immunogenetics in SZ. There is a relation between the severity of cognitive deficits and enhanced levels of inflammatory markers in schizophrenic patients, including C-reactive protein (CRP). Also, a relation between CRP and the negative-symptom subscale of Positive and Negative Syndrome Scale (PANSS) was observed.Aims To study the relation between CRP level with different cognitive domains in patients with SZ and its relation to the psychopathology of SZ.Methods A cross-sectional study was applied on 40 SZ patients and 40 healthy controls, serum CRP was measured, and they were cognitively assessed using Arabic version of Montreal Cognitive Assessment Basic (MoCA-B).Results SZ patients showed worse cognitive performance on all subtests (except orientation), MOCA-B, and the total score when compared with normal controls. A negative correlation between executive functions, calculation, abstraction, memory, naming, and attention subtests of MoCA-B and its total score with the serum CRP was found. A positive correlation between CRP and the negative subscale and total score of PANSS was found.Conclusions Serum CRP level was elevated in patients with SZ when compared with healthy controls and significantly negatively correlated with cognitive functions, and positively correlated with negative symptoms in SZ patients, which seconds the neuroinflammatory etiology of SZ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".