Characterizing Risk Factors of Neurocognitive Impairments in Post COVID-19 Condition
Bibliographic record
Abstract
ABSTRACT Introduction Post COVID-19 Condition (PCC) affects a substantial proportion of COVID-19 survivors and is frequently associated with persistent neurocognitive impairments. Despite its prevalence, the pathophysiological mechanisms underlying PCC-related neurocognitive decline remain unclear, although neuroinflammation is strongly implicated. Identifying key clinical and inflammatory risk factors associated with neurocognitive assessment scores in individuals with PCC will inform approach to care in clinical settings, support the development of diagnostic criteria, and guide targeted interventions. We hypothesized that individuals with lower MOCA and SDMT scores will exhibit elevated levels of systemic pro-inflammatory biomarkers compared to those with higher neurocognitive scores. Methods This retrospective, cross-sectional study examined 61 individuals with PCC recruited from Alberta Health Services Long COVID-19 Inter-Professional Outpatient Program, a specialized care center for post-COVID care. An additional 18 healthy controls were included for groupwise comparisons. Baseline demographic, clinical, and biochemical data were analyzed cross-sectionally alongside Montreal Cognitive Assessment (MOCA) and Symbol Digit Modalities Test (SDMT) scores. Multivariable linear regression was used to identify covariates associated with cognitive performance. To assess the likelihood of neurocognitive impairment within the PCC cohort, logistic regression was applied using established MOCA and SDMT cut-offs to classify participants as either neurocognitively impaired (NCI+) or unimpaired (NCI–). Group comparisons of demographics and biomarker levels were conducted using the Mann–Whitney U test and Fisher’s exact test. Results Older age was negatively associated, while female gender and higher education were positively associated with MOCA scores. SDMT scores were negatively associated with older age, PHQ-9 scores, and a prior diagnosis with depression or anxiety. Logistic regression analysis revealed that female gender was associated with lower odds of MOCA-defined neurocognitive impairment, whereas higher PHQ-9 scores and a history of depression or anxiety significantly increased the odds of SDMT-defined neurocognitive impairment. Serum IL-18 was the biomarker consistently associated with lower MOCA and SDMT scores in multivariable linear regression analysis, although it did not remain significant in logistic models. Groupwise comparisons confirmed IL-18 as the biomarker significantly elevated in NCI+ compared to NCI- individuals. Conclusion Neurocognitive impairments are highly prevalent deficits in PCC and are closely linked to mood disorders and the inflammatory biomarker, IL-18. This study highlights the importance of routine neurocognitive assessments in individuals with PCC, particularly those with risk factors such as older age, male gender, lower educational attainment, current or previous psychiatric vulnerabilities, and elevated serum IL-18 levels.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".