Lessons Learned From Conducting Internet-Based Randomized Clinical Trials During a Global Pandemic
Bibliographic record
Abstract
As the severe acute respiratory syndrome coronavirus 2 pandemic evolved, it was apparent that well designed and rapidly conducted randomized clinical trials were urgently needed. However, traditional clinical trial design presented several challenges. Notably, disease prevalence initially varied by time and region, and the pockets of outbreaks evolved geographically over time. Coupled with an occupational hazard from in-person study visits, timely recruitment would prove difficult in a traditional in-person clinical trial. Thus, our team opted to launch nationwide internet-based clinical trials using patient-reported outcome measures. In total, 2795 participants were recruited using traditional and social media, with screening and enrollment performed via an online data capture system. Follow-up surveys and survey reminders were similarly managed through this online system with manual participant outreach in the event of missing data. In this report, we present a narrative of our experience running internet-based clinical trials and provide recommendations for the design of future clinical trials during a world pandemic.
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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 | 0.005 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".