Trajectories of Inflammatory Markers and Post–COVID-19 Cognitive Symptoms
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Chronic systemic inflammation has been hypothesized to be a mechanistic factor leading to post-acute cognitive dysfunction after COVID-19. However, little data exist evaluating longitudinal inflammatory markers. METHODS: We conducted a secondary analysis of data collected from the CONTAIN randomized trial of convalescent plasma in patients hospitalized for COVID-19, including patients who completed an 18-month assessment of cognitive symptoms and PROMIS Global Health questionnaires. Patients with pre-COVID-19 dementia/cognitive abnormalities were excluded. Trajectories of serum cytokine panels, D-dimer, fibrinogen, C-reactive peptide (CRP), ferritin, lactate dehydrogenase (LDH), and absolute neutrophil counts (ANCs) were evaluated over 18 months using repeated measures and Friedman nonparametric tests. The relationships between the area under the curve (AUC) for each inflammatory marker and 18-month cognitive and global health outcomes were assessed. RESULTS: < 0.05), with the exception of IL-1β, which remained stable over time. There were no significant associations between the AUC for any inflammatory marker and 18-month cognitive symptoms, any neurologic symptom, or PROMIS Global Physical or Mental health T-scores. Receipt of convalescent plasma was not associated with any outcome measure. DISCUSSION: At 18 months posthospitalization for COVID-19, cognitive abnormalities were reported in 27% of patients, and below average PROMIS Global Mental and Physical Health scores occurred in 24% and 51%, respectively. However, there were no associations with measured inflammatory markers, which decreased over time.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".