Viral and atypical respiratory co‐infections in COVID‐19: a systematic review and meta‐analysis
Bibliographic record
Abstract
Objectives Respiratory co-infections have the potential to affect the diagnosis and treatment of COVID-19 patients. This meta-analysis was performed to analyze the prevalence of respiratory pathogens (viruses and atypical bacteria) in COVID-19 patients. Methods This review was consistent with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). Searched databases included: PubMed, EMBASE, Web of Science, Google Scholar, and grey literature. Studies with a series of SARS-CoV-2-positive patients with additional respiratory pathogen testing were included. Independently, 2 authors extracted data and assessed quality of evidence across all studies using Cochrane's Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology and within each study using the Newcastle Ottawa scale. Data extraction and quality assessment disagreements were settled by a third author. Pooled prevalence of co-infections was calculated using a random-effects model with univariate meta-regression performed to assess the effect of study subsets on heterogeneity. Publication bias was evaluated using funnel plot inspection, Begg's correlation, and Egger's test. Results Eighteen retrospective cohorts and 1 prospective study were included. Pooling of data (1880 subjects) showed an 11.6% (95% confidence interval [CI] = 6.9–17.4, I2 = 0.92) pooled prevalence of respiratory co-pathogens. Studies with 100% co-pathogen testing (1210 subjects) found a pooled prevalence of 16.8% (95% CI = 8.1–27.9, I2 = 0.95) and studies using serum antibody tests (488 subjects) found a pooled prevalence of 26.8% (95%, CI = 7.9–51.9, I2 = 0.97). Meta-regression found no moderators affecting heterogeneity. Conclusion Co-infection with respiratory pathogens is a common and potentially important occurrence in patients with COVID-19. Knowledge of the prevalence and type of co-infections may have diagnostic and management implications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".