Resilient Clinical Trial Infrastructure in Response to the COVID-19 Pandemic: Lessons Learned from the TOGETHER Randomized Platform Clinical Trial
Bibliographic record
Abstract
In response to the COVID-19 pandemic, clinical research groups across the world developed trial protocols to evaluate the safety and efficacy of treatments for COVID-19. Despite this initial enthusiasm, only a small portion of these protocols were implemented. Of those implemented, a fraction successfully recruited their target sample size to analyze and disseminate findings. More than a year and a half into the COVID-19 pandemic, only a few clinical trials evaluating treatments for COVID-19 have generated new evidence. Productive randomized platform clinical trials evaluating COVID-19 treatments may attribute their success to intentional investments in developing resilient clinical trial infrastructures. Health system resiliency discourse provides a conceptual framework for characterizing attributes for withstanding shocks. This framework may also be useful for contextualizing the attributes of productive clinical trials evaluating COVID-19 therapies. We characterize the successful attributes and lessons learned in developing the TOGETHER Trial infrastructure using a health system resiliency framework. This framework may be considered by clinical trialists aiming to build resilient trial infrastructures capable of responding rapidly and efficiently to global health threats.
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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 | 0.487 | 0.574 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".