Trends in the use of antibiotics for pharyngitis in Saudi Arabia
Bibliographic record
Abstract
INTRODUCTION: Pharyngitis is one of the most common diagnoses for antibiotic prescriptions worldwide. Antibiotics should be prescribed for bacterial pharyngitis to reduce its complications. The aims of this study were to assess antibiotic prescriptions for pharyngitis cases, and their relationship with physicians' knowledge regarding its diagnosis and management. METHODOLOGY: A cross-sectional study was conducted. First, prescriptions for pharyngitis cases using the modified Centor criteria was evaluated at primary care centers in Saudi Arabia. Second, physicians' knowledge of the modified Centor score and the diagnosis and management of pharyngitis was assessed using a self-administered questionnaire. RESULTS: Out of 104 pharyngitis cases, 79% (n = 82) were prescribed antibiotics, of which 28% were evidence-based prescriptions. First-line antibiotics were prescribed in 34% of patients, and second-line (broad-spectrum) antibiotics such as amoxicillin/clavulanate were prescribed in half of the patients. The main significant predictors of antibiotic prescriptions were age < 3 years (odds ratio, 0.13; 95% CI, 0.02 to 0.97), tonsillar exudate (odds ratio, 21.14; 95% CI, 2.88 to 155.09), and throat erythema (odds ratio, 9.30; 95% CI, 1.18 to 73.41). Overall, physicians (n = 29) had adequate knowledge about the modified Centor score and the management of pharyngitis. CONCLUSIONS: Most prescribed antibiotics for pharyngitis were unnecessarily prescribed; the majority being broad-spectrum antibiotics. Despite physicians' adequate knowledge of the modified Centor score and the management of pharyngitis, their practice failed to demonstrate that. Induction of the Saudi Antimicrobial Stewardship Program in the primary care centers, accessibility to diagnostic tools, and educational programs may help in reducing unnecessary antibiotic prescriptions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".