Current trend of antibiotic prescription and management for peritonsillar abscess: A <scp>cross‐sectional</scp> study
Bibliographic record
Abstract
Abstract Objective There are no consensus guidelines for managing peritonsillar abscess (PTA) despite its prevalence. In order to devise best practice guidelines, current practice patterns must first be established. Methods This was a cross‐sectional study, surveying Otolaryngology—Head & Neck Surgery trainees (residents and fellows) and consultant (academic and community). The primary outcome was the type and duration of outpatient antibiotic prescription. Secondary outcomes included differences in workup, management, prescription, and follow‐up. Results There were 57 respondents to the survey; 24 (42%) trainees (residents/fellows) and 33 (58%) consultants. On average, each respondent managed an average of 15.2 (SD 11.2) PTAs within the last year. All respondents prescribed oral antibiotics, with amoxicillin—clavulanic acid being the most common (61%). Trainees prescribed amoxicillin—clavulanic acid more often than consultants (n = 21, 88% vs n = 14, 42%, P = .0084), respectively. Duration of antibiotic therapy ranged from 5 to 14 days. Most commonly, a 10‐day course of antibiotics was prescribed (n = 31, 54%). Regarding the management of PTAs, a majority of respondents requested blood work (n = 39, 68%), performed needle aspiration (n = 42, 72%) and performed incision and drainage (n = 52, 91%). Culture and sensitivity of the aspirate/drainage fluid was frequently performed (n = 41, 72%). Patients were often provided non‐opioid analgesics (n = 46, 81%), but more than half still received prescription opioids (n = 36, 63%). The majority of clinicians arranged for follow‐up (n = 42, 74%), most often with Otolaryngology – Head & Neck Surgery (n = 27, 64%), with an average follow‐up of 12.5 (SD 8.2) days. Conclusion We found heterogeneity in the management of PTAs, with variability in the outpatient antibiotic prescription. This study highlighted the wide range of management strategies employed along with differences in workup, investigation, post‐discharge analgesic prescription, and follow‐up arrangements. Level of Evidence 5.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".