The effect of dual inflammation on the acute phase clinical outcomes of schizophrenia patients with comorbid COVID-19
Bibliographic record
Abstract
Background and hypothesis: Inflammation plays a crucial role in pathological mechanisms in schizophrenia (SZ) and the coronavirus disease 2019 (COVID-19), but the impact of dual inflammation on SZs' clinical outcomes is poorly understood. This study aimed to investigate whether dual inflammation impacts acute phase outcomes in patients with schizophrenia comorbid with COVID-19 (COVID-SZs). Study design: A total of 114 SZs and 49 COVID-SZs were recruited for this study. Plasma samples were collected and analyzed for levels of routine blood and inflammatory cytokines from all the participants. Then clinical symptoms, cognitive performance, and functional assessments were conducted at recruitment. One-way analysis of covariance examined the differences in inflammatory cytokines and correlation analyses examined the relationship between inflammatory cytokines and clinical outcomes. Study results: After controlling for age, gender, substance use status, and antipsychotic medications, levels of inflammatory cytokines increased in COVID-SZs groups compared to SZs groups. There were significantly higher total Positive and Negative Syndrome Scale (PANSS) scores and positive PANSS scores in COVID-SZs groups compared to SZs. As for cognitive performance, the COVID-SZs group had significantly worse performance in processing speed and attention than the SZs. The COVID-SZs group had significantly worse health status compared to the SZs. There were significantly different correlation patterns between the severity of psychiatric symptoms and inflammatory cytokines in COVID-SZs and SZs group. Conclusions: Findings indicate that dual inflammation exacerbates the acute phase clinical outcome of COVID-SZs. Suggesting a combined anti-inflammatory drug or the use of potentially anti-inflammatory antipsychotics in the acute phase of treatment to mitigate central nervous system damage. Regular monitoring of inflammatory marker levels can help reduce the risk of fluctuating psychiatric symptoms in patients with schizophrenia caused by inflammatory storms.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".