Barriers, Solutions, and Opportunities for Adapting Critical Care Clinical Trials in the COVID-19 Pandemic
Bibliographic record
Abstract
Importance: The COVID-19 pandemic created unprecedented challenges for clinical trials worldwide, threatening premature closure and trial integrity. Every phase of research operations was affected, often requiring modifications to protocol design and implementation. Objectives: To identify the barriers, solutions, and opportunities associated with continuing critical care trials that were interrupted during the pandemic, and to generate suggestions for future trials. Design, Setting, and Participants: This mixed-methods study performed an explanatory sequential analysis involving a self-administered electronic survey and focus groups of principal investigators (PIs) and project coordinators (PCs) conducting adult and pediatric individual-patient randomized trials of the Canadian Critical Care Trials Group during the COVID-19 pandemic. Eligible trials were actively enrolling patients on March 11, 2020. Data were analyzed between September 2023 and January 2024. Main Outcomes and Measures: Importance ratings of barriers to trial conduct and completion, solutions employed, opportunities arising, and suggested strategies for future trials. Quantitative data examining barriers were analyzed using descriptive statistics. Data addressing solutions, opportunities, and suggestions were analyzed by qualitative content analysis. Integration involved triangulation of data sources and perspectives about 13 trials, synthesized by an interprofessional team incorporating reflexivity and member-checking. Results: A total of 13 trials run by 29 PIs and PCs (100% participation rate) were included. The highest-rated barriers (on a 5-point scale) to ongoing conduct during the pandemic were decisions to pause all clinical research (mean [SD] score, 4.7 [0.8]), focus on COVID-19 studies (mean [SD] score, 4.6 [0.8]), and restricted family presence in hospitals (mean [SD] score, 4.1 [0.8]). Suggestions to enable trial progress and completion included providing scientific leadership, implementing technology for communication and data management, facilitating the informed consent process, adapting the protocol as necessary, fostering site engagement, initiating new sites, streamlining ethics and contract review, and designing nested studies. The pandemic necessitated new funding opportunities to sustain trial enrollment. It increased public awareness of critical illness and the importance of randomized trial evidence. Conclusions and Relevance: While underscoring the vital role of research in society and drawing the scientific community together with a common purpose, the pandemic signaled the need for innovation to ensure the rigor and completion of ongoing trials. Lessons learned to optimize research procedures will help to ensure a vibrant clinical trials enterprise in the future.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.211 | 0.430 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".