Most Common Pathogens Causing Rhinosinusitis in Patients Who Underwent Endoscopic Sinus Surgery Before, During, and After the COVID-19 Pandemic
Bibliographic record
Abstract
Importance Chronic rhinosinusitis (CRS) significantly impacts patients’ quality of life and incurs substantial healthcare costs. Understanding pathogen trends before, during, and after the COVID-19 pandemic can inform better management and treatment strategies. Objective To identify the common pathogens associated with CRS and compare them across pre-pandemic, during-pandemic, and post-pandemic periods. Design Retrospective chart review. Setting McGill University Health Centre, Montreal, Canada. Participants Around 147 patients were 18 years and older, diagnosed with CRS, underwent endoscopic sinus surgery within the specified timeframe (January 2017 to September 2023), and whose charts contained relevant microbiology information. Patients were categorized into 3 groups based on surgery dates: pre- (January 2018 to November 2019), during- (January 2020 to December 2021), and post-pandemic (February 2022 to September 2023). Main Outcome Measures Distribution and prevalence of pathogens associated with CRS across the 3 time periods. Microbiology results from nasal cultures were analyzed to identify predominant pathogens. Results Among the 147 patients, 46 distinct organisms were identified. Staphylococcus aureus was the most prevalent pathogen, increasing during the COVID-19 period (24.7%) compared to pre-pandemic (17.9%) and post-pandemic (21.5%) periods. Significant increases during the COVID-19 period were noted for Aspergillus fumigatus (6.8%, P < .001), Enterobacter cloacae (6.8%, P = .01), and Cutibacterium acnes (6.8%, P = .03). Post-pandemic, significant rises were observed in Serratia marcescens ( P < .001) and Achromobacter denitrificans ( P = .03). Conclusions and Relevance Significant shifts in CRS-associated pathogens occurred during the COVID-19 pandemic. Notable changes in the prevalence of S. aureus, A. fumigatus, E. cloacae, and C. acnes were observed during the pandemic, with increases in S. marcescens and A. denitrificans post-pandemic. These findings suggest that the pandemic’s impact on healthcare practices and environmental factors influenced the microbial etiologies of CRS. Future research may explore the mechanisms driving these changes and their long-term implications for CRS management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".