Most common concurrent infections with COVID-19
Bibliographic record
Abstract
Concurrent infections are a common complication of viral respiratory infections. They pose diagnostic challenges due to an overlap of similar symptoms, resistance to treatment and extending length of hospital stay. In this review we will extensively discuss the most common concurrent infections in patients with coronavirus disease 2019 (COVID-19). A thorough literature search was conducted in online databases such as PubMed, Google Scholar and included systematic reviews, meta-analyses, prospective and retrospective cohort studies in this review. Bacterial co-infections are the most common concurrent infections in patients with COVID-19 succeeded by viral and fungal co-infections. The prevalence of co-infections in COVID-19 patients is higher in intensive care units (ICU). Gram negative bacteria such as Klebsiella pneumoniae, Pseudomonas aeruginosa and Escherichia coli, whereas gram positive bacteria such as Staphylococcus aureus are common pathogens for bacterial co-infections. The findings for the most common viral co-infection are inconsistent however, larger number of studies report respiratory viral co-infections such as influenza and respiratory syncytial virus. Fungal co-infections in COVID-19 patients are most commonly caused by the Candida spp. and occur predominantly in patients admitted in the ICU and are associated with high mortality and morbidity in COVID-19 patients. Continuous research on concurrent infections occurring in COVID-19 is essential. Larger prospective studies based on stratified groups of age, gender, and both ICU and non-ICU settings should be conducted. Studies on microbial susceptibility can lend more weight to empirical antibiotic and antifungal therapy. Early diagnosis of concurrent infections in COVID-19 is imperative to prevent poor patient outcomes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".