Cognitive Assessment in SARS-CoV-2 Patients: A Systematic Review
Bibliographic record
Abstract
Background: Patients with post-infective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often show both short- and long-term cognitive deficits within the dysexecutive/inattentive spectrum. However, little is known about which cognitive alterations are commonly found in patients recovered from SARS-CoV-2, and which psychometric tools clinicians should consider when assessing cognition in this population. The present work reviewed published studies to provide a critical narrative of neuropsychological (NPs) deficits commonly observed after SARS-CoV-2 infection and the tests most suited for detecting such cognitive sequelae depending on illness severity. Methods: This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and was pre-registered on Prospective Register of Systematic Reviews (PROSPERO) (CRD42021253079). Observational studies quantitatively assessing cognition in patients with post-infective SARS-CoV-2 were considered. From 711 retrieved articles, 19 studies conducted on patients with SARS-CoV-2 without medical comorbidities were included and stratified by disease severity. Results: = 13) adopted first-level tests. The most frequently administered screeners were the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE)-with the former more likely to detect mild, and the latter moderate/severe deficits. Among second-level tests, those assessing attention and executive functions (EFs) were highly represented. Remotely-delivered tests yielded lower percentages of cognitive impairment. Overall, cognitive domains often found to be impaired were EFs, attention, and memory. Conclusion: Cognitive sequelae in patients with post-infective SARS-CoV-2 can be detected with NPs testing. Depending on the psychometric test features, the likelihood of observing cognitive deficits can vary. Further studies on larger sample sizes are needed to investigate the clinical usefulness of second-level tools. The primary goal of preventative health services should be the early detection and intervention of emerging cognitive deficits.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".