Cognitive function in recovered COVID-19 Lebanese patients with schizophrenia
Bibliographic record
Abstract
INTRODUCTION: It remains unclear whether COVID-19 which is an infectious disease caused by the SARS-CoV-2 virus is associated with the deterioration of cognitive function among patients with schizophrenia. This study aimed to evaluate changes in cognitive function before and after COVID-19 and associated factors among patients with schizophrenia at the Psychiatric Hospital of the Cross (HPC). METHODS: A prospective cohort study was conducted among 95 patients with schizophrenia followed from mid-2019 until June 2021 at the Psychiatric Hospital of the Cross (HPC). This cohort was divided into a group diagnosed with COVID-19 (n = 71) and another not diagnosed with COVID-19 (n = 24). The questionnaire included the Brief Assessment of Cognition in Schizophrenia (BACS), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS), and Activities of Daily Living (ADL). RESULTS: The repeated-measures ANOVA showed no significant effect of time and the interaction between time and being diagnosed or not with COVID-19 on cognition. However, being diagnosed or not with COVID-19 had a significant effect on global cognitive function (p = 0.046), verbal memory (p = 0.046), and working memory (p = 0.047). The interaction between being diagnosed with COVID-19 and cognitive impairment at baseline was significantly associated with a higher cognitive deficit (Beta = 0.81; p = 0.005). Clinical symptoms, autonomy, and depression were not associated with the cognition (p > 0.05 for all). CONCLUSION: COVID-19 disease affected global cognition and memory: patients diagnosed with COVID-19 had more deficits in these domains than those without COVID-19. Further studies are necessary to clarify the variation of cognitive function among schizophrenic patients with COVID-19.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".