Central nervous system biomarkers GFAp and NfL associate with post-acute cognitive impairment and fatigue following critical COVID-19
Bibliographic record
Abstract
A high proportion of patients with coronavirus disease 2019 (COVID-19) experience post-acute COVID-19, including neuropsychiatric symptoms. Objective signs of central nervous system (CNS) damage can be investigated using CNS biomarkers such as glial fibrillary acidic protein (GFAp), neurofilament light chain (NfL) and total tau (t-tau). We have examined whether CNS biomarkers can predict fatigue and cognitive impairment 3-6 months after discharge from the intensive care unit (ICU) in critically ill COVID-19 patients. Fifty-seven COVID-19 patients admitted to the ICU were included with analysis of CNS biomarkers in blood at the ICU and at follow up. Cognitive dysfunction and fatigue were assessed with the Montreal Cognitive Assessment (MoCA) and the Multidimensional Fatigue inventory (MFI-20). Elevated GFAp at follow-up 3-6 months after ICU discharge was associated to the development of mild cognitive dysfunction (p = 0.01), especially in women (p = 0.005). Patients who experienced different dimensions of fatigue at follow-up had significantly lower GFAp in both the ICU and at follow-up, specifically in general fatigue (p = 0.009), physical fatigue (p = 0.004), mental fatigue (p = 0.001), and reduced motivation (p = 0.001). Women showed a more pronounced decrease in GFAp compared to men, except for in mental fatigue where men showed a more pronounced GFAp decrease compared to women. NfL concentration at follow-up was lower in patients who experienced reduced motivation (p = 0.004). Our findings suggest that GFAp and NfL are associated with neuropsychiatric outcome after critical COVID-19.Trial registration The study was registered à priori (clinicaltrials.gov: NCT04316884 registered on 2020-03-13 and NCT04474249 registered on 2020-06-29).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".