The Surviving Sepsis Campaign: Research Priorities for Coronavirus Disease 2019 in Critical Illness
Bibliographic record
Abstract
OBJECTIVES: To identify research priorities in the management, pathophysiology, and host response of coronavirus disease 2019 in critically ill patients. DESIGN: The Surviving Sepsis Research Committee, a multiprofessional group of 17 international experts representing the European Society of Intensive Care Medicine and Society of Critical Care Medicine, was virtually convened during the coronavirus disease 2019 pandemic. The committee iteratively developed the recommendations and subsequent document. METHODS: Each committee member submitted a list of what they believed were the most important priorities for coronavirus disease 2019 research. The entire committee voted on 58 submitted questions to determine top priorities for coronavirus disease 2019 research. RESULTS: The Surviving Sepsis Research Committee provides 13 priorities for coronavirus disease 2019. Of these, the top six priorities were identified and include the following questions: 1) Should the approach to ventilator management differ from the standard approach in patients with acute hypoxic respiratory failure?, 2) Can the host response be modulated for therapeutic benefit?, 3) What specific cells are directly targeted by severe acute respiratory syndrome coronavirus 2, and how do these cells respond?, 4) Can early data be used to predict outcomes of coronavirus disease 2019 and, by extension, to guide therapies?, 5) What is the role of prone positioning and noninvasive ventilation in nonventilated patients with coronavirus disease?, and 6) Which interventions are best to use for viral load modulation and when should they be given? CONCLUSIONS: Although knowledge of both biology and treatment has increased exponentially in the first year of the coronavirus disease 2019 pandemic, significant knowledge gaps remain. The research priorities identified represent a roadmap for investigation in coronavirus disease 2019.
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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 | 0.101 | 0.071 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 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".