731. Investigating Clinical Factors Contributing to Continued Antibiotic Therapy in Patients with Viral Upper Respiratory Tract Infections
Bibliographic record
Abstract
Abstract Background It has previously been demonstrated that upwards of 50% of patients presenting to Emergency Departments with symptoms of an upper respiratory tract infection receive empirical antibiotics, and that even with a demonstrated viral infection, 70% of these patients are continued on antibiotics. However, the clinical and biochemical factors contributing to this continued therapy is unclear. This study assessed parameters that may impact antibiotic prescriptions in patients with a confirmed viral respiratory infection. Methods. Positive respiratory virus PCRs (RVPs) from nasopharyngeal aspirates performed on adult patients presenting to the McGill University Health Centre Emergency Departments and outpatient clinics over a period of 10 days during the peak of influenza season were included. For each patient, antibiotic administration pre- and post-PCR result were determined, as were the presence of leukocytosis, neutrophilia, an abnormal chest X-ray, and sepsis. Each parameter’s effect on antibiotic use was then determined. Results. During the study period, there were 123 positive RVPs included. These consisted of 34% Flu A, 43% Flu B, and 23% were a mixture of other common respiratory viruses. Antibiotics were administered in 38% of patients before the test was resulted and continued in 79% of these patients afterwards. There was no correlation between the presence of leukocytosis, neutrophilia, signs of sepsis or abnormalities on chest X-ray and continued antibiotic therapy. Conclusion. Despite identification of a respiratory virus infection, patients are routinely treated with antibiotics even without significant evidence of a bacterial process. The impact of testing for respiratory viruses in limiting antibiotic therapy could be improved by education and direct antibiotic stewardship interventions in this population. Disclosures All authors: No reported disclosures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".