Serum ferritin level during hospitalization is associated with Brain Fog after COVID-19
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) remains an epidemic worldwide. Most patients suffer residual symptoms, the so-called "Long COVID," which includes respiratory and neuropsychiatric symptoms. Brain Fog, one of the symptoms of Long COVID, is a major public health issue because it can impair patients' quality of life even after recovery from the disease. However, neither the pathogenesis nor the treatment of this condition remains unknown. We focused on serum ferritin levels in this study and collected information on the onset of Brain Fog through questionnaires and found that high ferritin levels during hospitalization were associated with the occurrence of Brain Fog. In addition, we excluded confounders as far as possible using propensity score analyses and found that ferritin was independently associated with Brain Fog in most of the models. We conducted phase analysis and evaluated the interaction of each phase with ferritin levels and Brain Fog. We found a positive correlation between serum ferritin levels during hospitalization and Brain Fog after COVID-19. High ferritin levels in patients with Brain Fog may reflect the contribution of chronic inflammation in the development of Brain Fog. This study provides a novel insight into the pathogenic mechanism of Brain Fog after COVID-19.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".