Echinacea Reduces Antibiotics by Preventing Respiratory Infections: A Meta-Analysis (ERA-PRIMA)
Bibliographic record
Abstract
Respiratory tract infections (RTIs) are the leading cause of antibiotic prescriptions, primarily due to the risk for secondary bacterial infections. In this study, we examined whether Echinacea could reduce the need for antibiotics by preventing RTIs and their complications, and subsequently investigated its safety profile. A comprehensive search of EMBASE, PubMed, Google Scholar, Cochrane DARE and clinicaltrials.gov identified 30 clinical trials (39 comparisons) studying Echinacea for the prevention or treatment of RTIs in 5652 subjects. Echinacea significantly reduced the monthly RTI occurrence, risk ratio (RR) 0.68 (95% CI 0.61–0.77) and number of patients with ≥1 RTI, RR = 0.75 [95% CI 0.69–0.81] corresponding to an odds ratio 0.53 [95% CI 0.42–0.67]. Echinacea reduced the risk of recurrent infections (RR = 0.60; 95% CI 0.46–0.80), RTI complications (RR = 0.44; 95% CI 0.36–0.54) and the need for antibiotic therapy (RR = 0.60; 95% CI 0.39–0.93), with total antibiotic therapy days reduced by 70% (IRR = 0.29; 95% CI 0.11–0.74). Alcoholic extracts from freshly harvested Echinacea purpurea were the strongest, with an 80% reduction of antibiotic treatment days, IRR 0.21 [95% CI 0.15–0.28]. An equal number of adverse events occurred with Echinacea and control treatment. Echinacea can safely prevent RTIs and associated complications, thereby decreasing the demand for antibiotics. Relevant differences exist between Echinacea preparations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".