Neuromuscular Complications of SARS-CoV-2 and Other Viral Infections
Bibliographic record
Abstract
In this article we review complications to the peripheral nervous system that occur as a consequence of viral infections, with a special focus on complications of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). We discuss neuromuscular complications in three broad categories; the direct consequences of viral infection, autoimmune neuromuscular disorders provoked by viral infections, and chronic neurodegenerative conditions which have been associated with viral infections. We also include discussion of neuromuscular disorders that are treated by immunomodulatory therapies, and how this affects patient susceptibility in the current context of the coronavirus disease 2019 (COVID-19) pandemic. COVID-19 is associated with direct consequences to the peripheral nervous system via presumed direct viral injury (dysgeusia/anosmia, myalgias/rhabdomyolysis, and potentially mononeuritis multiplex) and autoimmunity (Guillain Barré syndrome and variants). It has important implications for people receiving immunomodulatory therapies who may be at greater risk of severe outcomes from COVID-19. Thus far, chronic post-COVID syndromes (a.k.a: long COVID) also include possible involvement of the neuromuscular system. Whether we may observe neuromuscular degenerative conditions in the longer term will be an important question to monitor in future studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".