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 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 | 0.158 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".