Clinical Trials and Novel Pathogens: Lessons Learned from SARS
Bibliographic record
Abstract
During the recent global outbreak of severe acute respiratory syndrome (SARS), thousands of patients received treatments of uncertain efficacy and known toxicity such as ribavirin and corticosteroids. Despite this, no controlled clinical trials assessing the efficacy of these agents were conducted. If a second global SARS outbreak occurred, clinicians would not have controlled data on which to base therapeutic decisions. We discuss the unique methodologic and logistical challenges faced by researchers who attempt to conduct controlled trials of therapeutic agents during an outbreak of a novel or unknown infectious pathogen. We draw upon our own experience in attempting to conduct a randomized controlled trial (trial) of ribavirin therapy for SARS and discuss the lessons learned. Strategies to facilitate future clinical trials during outbreaks of unknown or novel pathogens are also presented.
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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 | 0.248 | 0.360 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".