Facial Nerve Paralysis and <scp>COVID</scp>‐19: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Several cases of facial nerve paralysis (FNP) post-COVID-19 infection have been reported with varying presentations and management. This study aims to identify FNP clinical characteristics and recovery outcomes among patients acutely infected with COVID-19. We hypothesize that FNP is a potentially unique sequalae associated with COVID-19 infections. METHODS: A systematic review of PubMed-Medline, OVID Embase, and Web of Science databases from inception to November 2021 was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: This search identified 630 studies with 53 meeting inclusion criteria. This resulted in 72 patients, of which 30 (42%) were diagnosed with Guillain-Barré Syndrome (GBS). Non-GBS patients were on average younger (36 vs. 53 years) and more likely to present with unilateral FNP (88%) compared to GBS patients who presented predominantly with bilateral FNP (74%). Among non-GBS patients, majority (70%) of FNP presented a median of 8 [IQR 10] days after the onset of initial COVID-19 symptom(s). Treatment for non-GBS patients consisted of steroids (60%), antivirals (29%), antibiotics (21%), and no treatment (21%). Complete FNP recovery in non-GBS patients was achieved in 67% patients within a median of 11 [IQR 24] days. CONCLUSION: FNP is a possible presentation post COVID-19 infections, associated with both GBS and non-GBS patients. Although no causation can be assumed, the clinical course of isolated FNP associated with COVID-19 raises the possibility of a unique presentation differing from Bell's palsy, seen with higher proportion of patients developing bilateral FNP and a shorter duration to complete recovery. Laryngoscope, 133:1007-1013, 2023.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".