Antibiotics Utilization for Community Acquired Pneumonia in a Community Hospital Emergency Department
Bibliographic record
Abstract
BACKGROUND: A local health authority in Canada implemented its own Antimicrobial Stewardship Program (ASP) which provide guidelines to clinicians to utilize when treating infectious diseases such as community-acquired pneumonia (CAP). Objectives: The primary objective is to describe antibiotic usage patterns at the community hospital's emergency department (ED) and to analyze the patterns in relation to ASP goals of reducing risk of infections, adverse drug events and antibiotic resistance, and to identify potential areas of improvement. METHODS: This retrospective chart review included 156 adult patients with a diagnosis of CAP admitted to a community hospital ED from December 1, 2015 to November 30, 2016. RESULTS: 50.6% patients were prescribed moxifloxacin across all severity of CAP patients. Low and moderate severity CAP patients were most often prescribed antibiotic duration > 7 days. In low, moderate and high severity CAP patients who were treated using ceftriaxone, 100%, 88.9% and 66.6% patients were treated with ceftriaxone 2000 mg daily respectively. CONCLUSIONS: Antibiotic prescribing patterns suggest fluoroquinolones were frequently being over-prescribed, ceftriaxone dosages were often too high, and duration of antibiotics for low and moderate severity CAP were too long. More efforts are needed to promote appropriate antibiotic usage and optimize patient care.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".