COVID-19 human challenge trials and randomized controlled trials: lessons for the next pandemic
Bibliographic record
Abstract
The COVID-19 pandemic touched off an unprecedented search for vaccines and treatments. Without question, the development of vaccines to prevent COVID-19 was an enormous scientific accomplishment. Further, the RECOVERY and Solidarity trials identified effective treatments for COVID-19. But all was not success. The urgent need for COVID-19 prevention and treatment fueled an embrace of risks—to research participants and to the reliability of the science itself—as allegedly necessary costs to speed scientific progress. Scientists and (even) ethicists supported overturning longstanding norms protecting healthy volunteers in human challenge trials to speed vaccine development, but these trials led to no vaccines. Physicians, with the approval of research ethics committees, designed hundreds of unblinded, single-center clinical trials at high risk of bias to speed the identification of new treatments. But these clinical trials led to no treatments. The lesson for future pandemics is that the acceptance of greater risks to participants or science does not reliably lead to progress. We are better served by science that upholds the highest ethical and methodological standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.559 | 0.738 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.034 | 0.039 |
| Insufficient payload (model declined to judge) | 0.012 | 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".