Higher Inflammatory Markers are correlated with Worse Cognitive Function in Coronavirus Disease-2019 Patients
Bibliographic record
Abstract
AIM: This study aimed to determine the correlation between inflammation with cognitive function in COVID-19 patients. METHODS: We recruited COVID-19 patients using consecutive sampling methods in Adam Malik General Hospital Medan, Indonesia. The neutrophil-to-lymphocyte ratio (NLR,) C-reactive protein (CRP), D-dimer, and ferritin serum levels were measured as inflammatory markers . Cognitive function was assessed in several cognitive domains using Forward Digit Span for attention, Bacward Digit Span for working memory, and Trail Making Test parts A and B for executive function. The correlation between inflammatory markers and cognitive function was analyzed using Spearman correlation test. RESULTS: This study involved 40 COVID-19 patients consisting of 13 (32.5%) males and 27 (67.5%) females; the median age of the patients was 39.5 (19–65) years. We found that higher D-dimer and ferritin levels were significantly correlated with worse BDS scores (r = −0.369 p = 0.019 and r = −0.408 p = 0.009, respectively) and higher ferritin level was also correlated with worse FDS score (r = −0.365 p = 0.020 and). Higher D-dimer and ferritin levels were also significantly correlated with a longer time of completion of TMT-B (r = 0.363 p = 0.022 and r = 0.433 p = 0.005) and higher ferritin level was also correlated with a longer time of completion of TMT-A (r = 0.438 P=0.005). There were no significant correlations between NLR and CRP levels with cognitive function. CONCLUSION: Higher inflammatory markers are correlated with worse attention, working memory, and executive function in COVID-19 patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".