Antibiotic prescribing patterns among patients admitted to an academic teaching hospital for COVID-19 during the first wave of the pandemic in Toronto: A retrospective, controlled study
Bibliographic record
Abstract
BACKGROUND: Empirical antibiotics are not recommended for coronavirus disease 2019 (COVID-19). METHODS: In this retrospective study, patients admitted to Toronto General Hospital’s general internal medicine from the emergency department for COVID-19 between March 1 and August 31, 2020 were compared with those admitted for community-acquired pneumonia (CAP) in 2020 and 2019 in the same months. The primary outcome was antibiotics use pattern: prevalence and concordance with COVID-19 or CAP guidelines. The secondary outcome was antibiotic consumption in days of therapy (DOT)/100 patient-days. We extracted data from electronic medical records. We used logistic regression to model the association between disease and receipt of antibiotics, linear regression to compare DOT. RESULTS: The COVID-19, CAP 2020, and CAP 2019 groups had 67, 73, and 120 patients, respectively. Median age was 71 years; 58.5% were male. Prevalence of antibiotic use was 70.2%, 97.3%, and 90.8% for COVID-19, CAP 2020, and CAP 2019, respectively. Compared with CAP 2019, the adjusted odds ratio (aOR) for receiving antibiotics was 0.23 (95% CI 0.10 to 0.53, p = 0.001) and 3.42 (95% CI 0.73 to 15.95, p = 0.117) for COVID-19 and CAP 2020, respectively. Among patients receiving antibiotics within 48 hours of admission, compared with CAP 2019, the aOR for guideline-concordant combination regimens was 2.28 (95% CI 1.08 to 4.83, p = 0.031) for COVID-19, and 1.06 (95% CI 0.55 to 2.05, p = 0.856) for CAP 2020. Difference in mean DOT/100 patient-days was –24.29 ( p = 0.009) comparing COVID-19 with CAP 2019, and +28.56 ( p = 0.003) comparing CAP 2020 with CAP 2019. CONCLUSIONS: There are opportunities for antimicrobial stewardship to address unnecessary antibiotic use.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".