Meeting the Challenges of Sepsis in Severe Coronavirus Disease 2019: A Call to Arms
Bibliographic record
Abstract
Sepsis is a life-threatening organ dysfunction that is caused by a dysregulated host response to infection. Sepsis may be caused by bacterial, fungal, or viral pathogens. The clinical manifestations exhibited by patients with severe coronavirus disease 2019 (COVID-19)-related sepsis overlap with those exhibited by patients with sepsis from secondary bacterial or fungal infections and can include an altered mental status, dyspnea, reduced urine output, tachycardia, and hypotension. Critically ill patients hospitalized with severe acute respiratory syndrome coronavirus 2 infections have increased risk for secondary bacterial and fungal infections. The same risk factors that may predispose to sepsis and poor outcome from bloodstream infections (BSIs) converge in patients with severe COVID-19. Current diagnostic standards for distinguishing between (1) patients who are critically ill, septic, and have COVID-19 and (2) patients with sepsis from other causes leave healthcare providers with 2 suboptimal choices. The first choice is to empirically administer broad-spectrum, antimicrobial therapy for what may or may not be sepsis. Such treatment may not only be ineffective and inappropriate, but it also has the potential to cause harm. The development of better methods to identify and characterize antimicrobial susceptibility will guide more accurate therapeutic interventions and reduce the evolution of new antibiotic-resistant strains. The ideal diagnostic test should (1) be rapid and reliable, (2) have a lower limit of detection than blood culture, and (3) be able to detect a specific organism and drug sensitivity directly from a clinical specimen. Rapid direct detection of antimicrobial-resistant pathogens would allow targeted therapy and result in improved outcomes in patients with severe COVID-19 and sepsis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.025 | 0.039 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".