A pilot study of the impact of bacterial and fungal coinfections on mildly ill COVID-19 patients
Bibliographic record
Abstract
Background Coinfections and superinfections significantly impair prognosis in severely ill COVID-19 patients who may develop ventilator-associated pneumonia. However, the role of bacterial and fungal infections and/or lung colonization in patients with moderate COVID-19 who are not on mechanical ventilation remains controversial. Additionally, there is limited data on the impact of coinfections on pneumonia development in vaccinated subjects. To clarify this question, we summarize the data for patients treated in the single infectious department for a moderate form of COVID-19-associated pneumonia. Methods We evaluated the association of the medical condition on hospital admission and disease duration with anti- Chlamydophila pneumoniae and anti- Mycoplasma pneumoniae quantitative IgM and sputum culture results in COVID-19 in patients (n=271). Results Non-pneumococcal Streptococci were the most frequent bacteria isolated from sputum (70% of the population; only one case of St. pneumoniae ), followed by Candida albicans (15.6% of the population) and Neisseria spp. (13% of the population). Airway colonization with C. albicans and anti- M. pneumoniae IgM seropositivity was significantly associated with a higher CT score, especially in vaccinated patients; meanwhile, fungal pathogen C. albicans colonization was associated with prolonged hospital duration. Airway colonization with C. albicans was associated with slightly longer disease duration. Conclusion The results demonstrate that respiratory pathogens, at least M. pneumoniae , can contribute to the risk of COVID-19 onset and/or severity in the vaccinated population. Meanwhile, neither bacterial agents of atypical pneumonia nor lung colonization with opportunistic pathogens are essential for recovery in patients with moderate COVID-19 infection when appropriate treatment is provided.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".