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 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 | 0.001 |
| 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".