COVID-19, SARS and MERS: A neurological perspective
Bibliographic record
Abstract
Central to COVID-19 pathophysiology is an acute respiratory infection primarily manifesting as pneumonia. Two months into the COVID-19 outbreak, however, a retrospective study in China involving more than 200 participants revealed a neurological component to COVID-19 in a subset of patients. The observed symptoms, the cause of which remains unclear, included impaired consciousness, skeletal muscle injury and acute cerebrovascular disease, and appeared more frequently in severe disease. Since then, findings from several studies have hinted at various possible neurological outcomes in COVID-19 patients. Here, we review the historical association between neurological complications and highly pathological coronaviruses including SARS-CoV, MERS-CoV and SARS-CoV-2. We draw from evidence derived from past coronavirus outbreaks, noting the similarities and differences between SARS and MERS, and the current COVID-19 pandemic. We end by briefly discussing possible mechanisms by which the coronavirus impacts on the human nervous system, as well as neurology-specific considerations that arise from the repercussions of COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".