Seasonal Variation of Peritonsillar Abscess Presentation to an Emergency Department in an Atlantic Canadian Climate: A Retrospective Review
Bibliographic record
Abstract
Objectives: Patients with a peritonsillar abscess (PTA) often present to emergency departments as the first point of medical contact. Upper respiratory tract infections (URTIs) are more frequent in the winter. Therefore, we hypothesize that the incidence of PTAs will be more frequent in colder winter months as well. This is the first study assessing the seasonal variation and epidemiology of PTA presentations to an emergency department in Atlantic Canada, home to a unique maritime climate. Methods: A retrospective cohort study was conducted through a chart review of all patients who presented to the Saint John Regional Hospital Emergency Department from January 1, 2015, to December 31, 2020. Patient characteristics, treatment, and microbiology were reported. A chi-square goodness-of-fit test assessed the seasonal variation of PTA. Pearson correlations assessed PTA incidence per mean monthly temperature and humidity. Results: A total of 75 patients were included. 57.3% were male and 42.7% were female, with a mean age (±SD) of 35.9 ± 14.0. Most patients presented afebrile (82.7%, cutoff ≥ 38.0°C). Approximately half of all patients had an elevated WBC count (49.3%, cutoff ≥ 10.9 × 10 9 ). The most common bacteria isolated were Streptococcus species followed by anaerobic bacteria (17.9%). No significant variation was found with respect to season (X 2 (3) = 1.0, P = .801), temperature ( r(70) = 0.198, P = .096), or humidity, ( r(70) = 0.063, P = .599). Conclusion: This study did not find a seasonal variation of PTA in a maritime climate. These findings question the anecdotal hypothesis that PTA is associated with progression from acute URTIs and therefore would be more common in the winter months.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".