Impact of SARS-CoV-2 Infection on Cognitive Function: A Systematic Review
Bibliographic record
Abstract
The prevalence and etiology of COVID-19's impact on brain health and cognitive function is poorly characterized. With mounting reports of delirium, systemic inflammation, and evidence of neurotropism, a statement on cognitive impairment among COVID-19 cases is needed. A substantial literature has demonstrated that inflammation can severely disrupt brain function, suggesting an immune response, a cytokine storm, as a possible cause of neurocognitive impairments. In this light, the aim of the present study was to summarize the available knowledge of the impact of COVID-19 on cognition (i.e., herein, we broadly define cognition reflecting the reporting on this topic in the literature) during the acute and recovery phases of the disease, in hospitalized patients and outpatients with confirmed COVID-19 status. A systematic review of the literature identified six studies which document the prevalence of cognitive impairment, and one which quantifies deficits after recovery. Pooling the samples of the included studies (total sample n = 644) at three standards of quality produced conservative estimates of cognitive impairment ranging from 43.0 to 66.8% prevalence in hospitalized COVID-19 patients only, as no studies which report on outpatients met criteria for inclusion in the main synthesis. The most common impairment reported was delirium and frequent reports of elevated inflammatory markers suggest etiology. Other studies have demonstrated that the disease involves marked increases in IL-6, TNFα, and IL-1β; cytokines known to have a profound impact on working memory and attention. Impairment of these cognitive functions is a characteristic aspect of delirium, which suggests these cytokines as key mediators in the etiology of COVID-19 induced cognitive impairments. Researchers are encouraged to assay inflammatory markers to determine the potential role of inflammation in mediating the disturbance of cognitive function in individuals affected by 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".