The Effect on Mortality of Bacterial Co-Infections on Critically Ill Patients with Community-Acquired COVID-19 and Influenza Pneumonia: A Systematic Review
Bibliographic record
Abstract
Background: Bacterial co-infections in patients with viral pneumonia might increase mortality. In this study we aimed to evaluate their effect on the mortality of critically ill patients with viral pneumonia. Methods: A systematic search was conducted in PubMed, Web of Science, Scopus and Cochrane from inception until 30 March 2025. We included studies comparing the effect on mortality of bacterial co-infections in critically ill patients with viral pneumonia. The risk of bias was assessed by the Newcastle–Ottawa Scale. Results: From 3643 studies, 10 were included in our study with a total of 2862 COVID-19 patients and 4573 influenza patients. Seven studies were retrospective and three prospective. In total, 359/2862 of the COVID-19 and 904/4573 of the influenza patients were co-infected. Co-infections increased mortality in five out of the six studies evaluating COVID-19 patients and in two out of the eight studies evaluating influenza patients. Conclusions: The majority of the included studies were retrospective, which may limit the accuracy of these results. The exclusion of non-English literature may have led to the omission of relevant data. Based on our results, the impact of bacterial co-infection may be more pronounced in patients with COVID-19 pneumonia admitted to the ICU than in patients with influenza pneumonia.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".