Long‐term neurological dysfunction associated with <scp>COVID</scp> ‐19: Lessons from influenza and inflammatory diseases?
Bibliographic record
Abstract
As the COVID-19 pandemic persists, SARS-CoV-2 infection is increasingly associated with long-term neurological side effects including cognitive impairment, fatigue, depression, and anxiety, colloquially known as "long-COVID." While the full extent of long-COVID neuropathology across years or even decades is not yet known, we can perhaps take direction from long-standing research into other respiratory diseases, such as influenza, that can present with similar long-term neurological consequences. In this review, we highlight commonalities in the neurological impacts of influenza and COVID-19. We first focus on the common potential mechanisms underlying neurological sequelae of long-COVID and influenza, namely (1) viral neurotropism and (2) dysregulated peripheral inflammation. The latter, namely heightened peripheral inflammation leading to central nervous system dysfunction, is emerging as a shared mechanism in various peripheral inflammatory or inflammation-associated diseases and conditions. We then discuss historical and modern examples of influenza- and COVID-19-associated cognitive impairment, depression, anxiety, and fatigue, revealing key similarities in their neurological sequelae. Although we are learning that the effects of influenza and COVID differ somewhat in terms of their influence on the brain, as the impacts of long-COVID grow, such comparisons will likely prove valuable in guiding ongoing research into long-COVID, and perhaps foreshadow what could be in store for individuals with COVID-19 and their brain health.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".