Lessons learned from the <scp>CONCOR</scp>‐1 trial
Bibliographic record
Abstract
Faced with an evolving pandemic and a lack of clarity of the role of convalescent plasma for patients with COVID-19, the CONCOR-1 trial was launched. In 14 months the trial was designed, launched, completed, and submitted for publication. In total, 72 sites in three countries served by four blood suppliers randomised 940 patients. Many enablers facilitated the trial including: three study principal investigators to distribute the trial workload, diverse steering committee members, an international data safety monitoring committee, multiple statisticians and methodologists, virtual meeting platforms, REDCap data platform, pausing of non-COVID-19 trials, rapid approval pathways for institutional review boards and regulators, centralised institutional review boards in many locations, restriction of use of convalescent plasma to trial participants and the incredible dedication by research personnel. In future pandemics, we need to be prepared for rapid launch of trials. The protocols, consent forms, data collection tools, and procedures need to be in draft form ready for use at all times. We were well-prepared for blood shortages but should have anticipated the need to conduct trials with convalescent plasma. In this short article, we detail our lessons learned to inform researchers faced with the next pandemic pathogen.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".