Bacterial Superinfections Among Persons With Coronavirus Disease 2019: A Comprehensive Review of Data From Postmortem Studies
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Bibliographic record
Abstract
Abstract Background Limited clinical data suggest a ~16% prevalence of bacterial superinfections among critically ill patients with coronavirus disease 2019 (COVID-19). Methods We reviewed postmortem studies of patients with COVID-19 published in English through September 26, 2020, for histopathologic findings consistent with bacterial lung infections. Results Worldwide, 621 patients from 75 studies were included. The quality of data was uneven, likely because identifying superinfections was not a major objective in 96% (72/75) of studies. Histopathology consistent with a potential lung superinfection was reported in 32% (200/621) of patients (22–96 years old; 66% men). Types of infections were pneumonia (95%), abscesses or empyema (3.5%), and septic emboli (1.5%). Seventy-three percent of pneumonias were focal rather than diffuse. The predominant histopathologic findings were intra-alveolar neutrophilic infiltrations that were distinct from those typical of COVID-19-associated diffuse alveolar damage. In studies with available data, 79% of patients received antimicrobial treatment; the most common agents were beta-lactam/beta-lactamase inhibitors (48%), macrolides (16%), cephalosoprins (12%), and carbapenems (6%). Superinfections were proven by direct visualization or recovery of bacteria in 25.5% (51/200) of potential cases and 8% of all patients in postmortem studies. In rank order, pathogens included Acinetobacter baumannii, Staphylococcus aureus, Pseudomonas aeruginosa, and Klebsiella pneumoniae. Lung superinfections were the cause of death in 16% of potential cases and 3% of all patients with COVID-19. Conclusions Potential bacterial lung superinfections were evident at postmortem examination in 32% of persons who died with COVID-19 (proven, 8%; possible, 24%), but they were uncommonly the cause of death.
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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 | 0.000 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.006 |
| 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 it