Neurological and Head/Eyes/Ears/Nose/Throat Manifestations of COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Coronavirus disease 2019 (COVID-19) has been associated with various neurological and atypical head/eyes/ears/nose/throat (HEENT) manifestations. We sought to review the evidence for these manifestations. METHODS: In this systematic review and meta-analysis, we compiled studies published until March 31, 2021 that examined non-respiratory HEENT, central, and peripheral nervous system presentations in COVID-19 patients. We included 477 studies for qualitative synthesis and 59 studies for meta-analyses. RESULTS: Anosmia, ageusia, and conjunctivitis may precede typical upper/lower respiratory symptoms. Central nervous system (CNS) manifestations include stroke and encephalopathy, potentially with brainstem or cranial nerve involvement. MRI studies support CNS para-/postinfectious etiologies, but direct neuroinvasion seems very rare, with few cases detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in the CNS. Peripheral nervous system (PNS) manifestations include muscle damage, Guillain-Barre syndrome (GBS), and its variants. There was moderate-to-high study heterogeneity and risk of bias. In random-effects meta-analyses, anosmia/ageusia was estimated to occur in 56% of COVID-19 patients (95% CI: 0.41-0.71, I2:99.9%), more commonly than in patients without COVID-19 (OR: 14.28, 95% CI: 8.39-24.29, I2: 49.0%). Neurological symptoms were estimated to occur in 36% of hospitalized patients (95% CI: 0.31-0.42, I2: 99.8%); ischemic stroke in 3% (95% CI: 0.03-0.04, I2: 99.2%), and GBS in 0.04% (0.033%-0.047%), more commonly than in patients without COVID-19 (OR[stroke]: 2.53, 95% CI: 1.16-5.50, I2: 76.4%; OR[GBS]: 3.43,1.15-10.25, I2: 89.1%). CONCLUSIONS: Current evidence is mostly from retrospective cohorts or series, largely in hospitalized or critically ill patients, not representative of typical community-dwelling patients. There remains a paucity of systematically gathered prospective data on neurological manifestations. Nevertheless, these findings support a high index of suspicion to identify HEENT/neurological presentations in patients with known COVID-19, and to test for COVID-19 in patients with such presentations at risk of infection.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".