Effectiveness and cost-effectiveness of antibiotic treatments for community acquired pneumonia (CAP) and acute exacerbations of chronic bronchitis (AECB).
Bibliographic record
Abstract
BACKGROUND: The antibacterial activity, tolerability profile and duration of treatment associated with antibiotics are important therapy attributes when considering treating patients for lower respiratory tract infections (LRTIs), such as community acquired pneumonia (CAP) and acute exacerbations of chronic bronchitis (AECB). OBJECTIVES: To investigate the effectiveness and cost-effectiveness of oral antibiotics used in the treatment of LRTIs. METHODS: A cohort of inhaled corticosteroids users who were diagnosed with a LRTI and dispensed a prescription for one of the antibiotics under study on the same day as the diagnosis was selected from the administrative health databases of the Régie de l'assurance maladie du Québec (RAMQ). The risks of treatment failure were estimated using a logistic regression analysis. Treatment failure was defined as another prescription for any antibiotic, an emergency room visit or hospitalization for LRTIs, or death, in the 20 days following the dispensation of the first antibiotic prescribed. A cost-minimization analysis was performed in which only the drug costs related to the first antibiotic filled were considered. RESULTS: A total of 3,610 episodes of LRTIs were studied. There were no significant differences between antibiotics in terms of their respective adjusted odds ratios for rates of failure. However, the lower cost associated with azithromycin was significantly different from the costs associated with any other antibiotic (p<0.0001). CONCLUSION: Clinical effectiveness appears to be similar amongst second line antibiotics that are commonly used in the treatment of LRTIs in the community. Using a cost-minimization analysis, azithromycin appears to be the most cost-effective antibiotic treatment in this setting.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".