Analysis of the Relationship among Cognitive Impairment, Nutritional Indexes and the Clinical Course among COVID-19 Patients Discharged from Hospital—Preliminary Report
Bibliographic record
Abstract
Numerous data indicate the presence of cognitive impairment in people who have undergone COVID-19, often called COVID Fog (CF). This phenomenon persists even 6 months after infection, and its etiology and pathogenesis are not fully known. The aim of this article was to analyze the relationship among cognitive functioning, clinical data and nutrition indexes in patients discharged from the COVID-19 hospital of the Military Institute of Medicine, Warsaw, Poland. The sample comprised 17 individuals-10 women and 7 men, with ages of 65 ± 14 years. Cognitive impairment was measured with the use of the Montreal Cognitive Assessment (MoCA). The nutrition parameters included: hemoglobin, red blood cells, total cholesterol and its fractions, triglycerides, total protein, albumin, urea, creatinine, phosphates, calcium and sodium. The analysis showed that albumin concentration significantly correlated with the total MoCA score and especially with the short-term memory test score. Conversely, total cholesterol, and especially LDL concentrations, were highly and negatively associated with the MoCA score. In conclusion: markers of nutritional status are correlated with the severity of CF. Individuals with malnutrition or risk of malnutrition should be screened for CF. Further studies need to be performed in this area.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".