Impact of The COVID-19 Pandemic on Salivary Gland-Related Healthcare Interventions; A Systematic Review And Meta-Analysis
Bibliographic record
Abstract
Background: The emergence of SARS-CoV-2 variants has raised concerns regarding their potential impact on perioperative outcomes. Its effect on patients undergoing surgery for salivary gland diseases remains unclear. This systematic review and meta-analysis aimed to evaluate the impact of the COVID-19 pandemic on salivary gland-related healthcare interventions, including cancer treatments, sialendoscopy procedures, and parotid surgery outcomes. Materials and Methods: Following PRISMA guidelines, a systematic search was conducted in PubMed, Embase, and Web of Science (2019-2025) for studies reporting pre- and during-COVID data. Two reviewers independently screened records, extracted data, and assessed risk of bias using the Newcastle-Ottawa Scale. A random-effects meta-analysis was performed to pool odds ratios (ORs) for intervention outcomes. Results: Four studies (n=7,740 participants) were included. The pooled OR for salivary gland interventions during versus pre-COVID was 1.08 (95% CI: 0.88-1.33, P=0.45), indicating no significant change, with moderate heterogeneity (I²=46%). Subgroup analyses revealed increased odds of wound dehiscence post-parotid surgery (OR=4.40, 95% CI: 1.18-16.40) but no significant differences in delayed cancer diagnosis or urgent sialendoscopy. Conclusion: The COVID-19 pandemic did not significantly alter overall salivary gland intervention rates or adverse events, though some procedural complications increased non-significantly. Limited evidence underscores the need for larger, standardized studies. While this shows that surgeons maintained quality of practice in this era during the COVID-19.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.049 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".