Viral infections in critical care: a narrative review
Bibliographic record
Abstract
Viral infections form a substantial part of the intensive care workload, even before the recent and ongoing COVID-19 pandemic. The growing availability of molecular diagnostics for viral infections has led to increased recognition of these pathogens. This additional information, however, provides new challenges for interpretation and management. As the SARS-CoV-2 pandemic has amply demonstrated, the emergence and global spread of novel viruses are likely to provide continued challenges for critical care physicians into the future. This article will provide an overview of viral infections relevant to the critical care physician, discussing the diagnosis and management of respiratory viral infections, blood borne and enteric viruses. We will also discuss herpesviridae complications, commonly seen due to reactivation of latent infections. Further, we explore some rarer and emerging viruses, including recognition of viral haemorrhagic fevers, and briefly discuss post-viral syndromes which may present to the intensive care unit. Finally, we will discuss infection control and its importance in preventing nosocomial viral transmission.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".