Principles Guiding Nonpandemic Critical Care Research During a Pandemic
Bibliographic record
Abstract
OBJECTIVES: To describe the importance of critical care clinical research that is not pandemic-focused during pandemic times; outline principles to assist in the prioritization of nonpandemic research during pandemic times; and propose a guiding framework for decisions about whether, when and how to continue nonpandemic research while still honoring the moral and scientific imperative to launch research that is pandemic-focused. DESIGN/DATA SOURCES: Using in-person, email, and videoconference exchanges, we convened an interprofessional clinical research group, conducted a literature review of empirical studies, ethics documents and expert commentaries (2010 to present), and viewed traditional and social media posts (March 2020 to May 2020). Stakeholder consultation involved scientific, ethics, clinical, and administrative leaders. SETTING: Clinical research in the ICU. PATIENTS: Patients with and without coronavirus disease 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: While clinical research should be prioritized to advantage patients with coronavirus disease 2019 in order to care for affected patients, it ideally would not unduly disadvantage patients without coronavirus disease 2019. Thus, timely, rigorous, relevant, and ethical clinical research is needed to improve the care and optimize outcomes for both patients with and without coronavirus disease 2019, acknowledging how many studies that are not exclusively focused on coronavirus disease 2019 remain relevant to patients with coronavirus disease 2019. Considerations to continue nonpandemic-focused research include the status of the pandemic, local jurisdictional guidance, capacity and safety of bedside and research personnel, disposition of patients already enrolled in nonpandemic studies, analyzing characteristics of each nonpandemic-focused study, research oversight, and final reporting requirements. CONCLUSIONS: Deliberation about continuing nonpandemic research should use objective, transparent criteria considering several aspects of the research process such as bedside and research staff safety, infection control, the informed consent model, protocol complexity, data collection, and implementation integrity. Decisions to pause or pursue nonpandemic research should be proportionate, transparent, and revisited as the pandemic abates.
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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 | 0.726 | 0.534 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.016 | 0.119 |
| Scholarly communication | 0.036 | 0.022 |
| Open science | 0.012 | 0.025 |
| Research integrity | 0.046 | 0.060 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".