Post-Pandemic shifts in peritonsillar abscess: incidence and microbiological trends following the cessation of COVID-19-related nonpharmaceutical interventions
Bibliographic record
Abstract
Background The impact of COVID-19-related nonpharmaceutical interventions (NPI) on the bacterial composition of upper airway infections remains largely unexplored.Objectives We aimed to investigate the incidence and microbiology of peritonsillar abscess (PTA) following the cessation of NPI and to compare these findings with the periods before and during NPI implementation.Methods We performed a cross-sectional analysis of all PTA cases and their microbiological findings from 12 March, 2018 to 11 March, 2024, among patients admitted to the Ear-Nose-Throat Department, Aarhus University Hospital. Patients were categorised into three two-year periods in relation to NPI. Age-stratified population data for the catchment area were sourced from Statistics Denmark.Results A total of 1,030 patients were included. The annual incidence rate of PTA was significantly higher post-NPI (26.9 cases/100,000) compared to both the NPI period (14.9 cases/100,000, p < 0.001) and the pre-NPI period (21.8 cases/100,000, p = 0.003). Increased post-NPI rates were observed across all age groups. The number of cases positive for Streptococcuspyogenes and Fusobacterium necrophorum increased post-NPI (n = 102 and n = 89, respectively) compared to during the NPI period (n = 28 and n = 64, p < 0.001 and p = 0.052, respectively) and pre-NPI (n = 67 and n = 60, p = 0.009 and p = 0.021, respectively). Statistically non-significant increasing trends were found for less prevalent bacteria.Conclusion Following NPI cessation, PTA incidence rates surpassed both the NPI and pre-NPI levels. The rising PTA incidence rates post-NPI were primarily driven by an increasing number of cases positive for S. pyogenes and F. necrophorum, suggesting an immunity debt to these prevalent pathogens.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".