Facial Nerve Palsy Amid the SARS-CoV-2 Pandemic: A Pooled Analysis
Bibliographic record
Abstract
Importance Idiopathic facial nerve palsy (FNP) has devastating sequelae and is potentially linked to coronavirus disease-19 (COVID-19). Objective The rate of FNP was compared in the pandemic versus pre-pandemic periods. Furthermore, the risk of FNP was estimated among the COVID-19 vaccinated group. Design Systematic review and meta-analysis. Setting An electronic search was conducted in 7 databases: Scopus, Web of Science core collection, PubMed, Cochrane Central Register of Controlled Trials, MEDLINE, Embase, and CINAHL. Participants English observational studies investigating an association between idiopathic FNP and COVID-19 or its vaccination were included, irrespective of patients’ demographics. Exposures COVID-19 or COVID-19 vaccine. Main Outcome Measures Change in FNP incidence between the pre-pandemic and pandemic periods; risk of developing FNP in individuals vaccinated against COVID-19 compared to those who were unvaccinated against COVID-19. Results After excluding duplicates, the search yielded 906 related articles, of which 118 articles were included. The risk of FNP was statistically significantly higher during the COVID-19 pandemic than the pre-pandemic period (RR: 1.68, [95% CI: 1.16-2.43], P = .01). A nonsignificant increase in FNP risk was identified among COVID-19 vaccinated individuals compared to unvaccinated individuals (overall OR: 1.07, [95% CI: 0.85-1.35], P = .55). Conclusions and Relevance A remarkable increase in FNP rates was identified during the pandemic compared to pre-pandemic, which seemed unlikely to be attributed to COVID-19 vaccination.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.051 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".