You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:

- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.
- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.
- Prevention: The content covers measures to prevent the spread of COVID-19.
- Mechanism: The paragraph explains the biological mechanisms of the virus.
- Case Report: The content is a report of a COVID-19 case.
- Transmission: The paragraph discusses how COVID-19 is transmitted.
- Diagnosis: The content is about diagnosing COVID-19 in individuals.

Your classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: ["Topic1", "Topic2", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.

Article:

Background: Balancing the use of antibacterial therapy against selection for resistance in this pandemic era has introduced both questions and guidelines. In this project, we explored how prescription of empirical antibacterial therapy differs between those with and without SARS-CoV-2 infection. Methods: Multivariable logistic regression was used to determine whether COVID-19 status and other factors play a role in the prescription of antibacterial therapy in an inpatient setting at a large referral academic medical centre. Further analysis was conducted to determine whether these factors differ between those testing positive and negative for SARS-CoV-2. Results: Of 405 patients in the cohort, 175 received antibacterial therapy and 296 tested positive for SARS-CoV-2. A positive SARS-CoV-2 test carried an OR of 0.3 (95% CI: 0.19, 0.49) for receiving antibacterial treatment in the first 48 h after admission (P < 0.0001) adjusting for age and procalcitonin results. Patients were 1% and 3% less likely to receive antibacterials for every year increase in age in the overall group and among those testing negative for SARS-CoV-2, respectively. Younger age was found to impact use of antibacterial therapy in both the overall analysis as well as the SARS-CoV-2 negative subgroup (P = 0.03 and P = 0.01). High procalcitonin values were found to be associated with increased antibacterial therapy use in both the overall and stratified analyses. Conclusions: Antibacterial therapy prescription differs by COVID-19 disease status, and procalcitonin results are most highly associated with antibacterial use across strata.
["Treatment"]