Platform trials as the way forward in infectious disease’ clinical research: the case of coronavirus disease 2019
Bibliographic record
Abstract
Funding Information: The REMAP-CAP trial is funded by the Platform for European Preparedness Against Re-emerging Epidemics consortium by the European Union , FP7-HEALTH-2013-INNOVATION-1 (grant 602525 ), the Australian National Health and Medical Research Council (grant APP1101719 ), the New Zealand Health Research Council (grant 16/631 ), the Canadian Institute of Health Research Strategy for Patient-Oriented Research Innovative Clinical Trials Programme (grant 158584 ), the UK National Institute for Health Research ( NIHR ) and the NIHR Imperial Biomedical Research Centre , the Health Research Board of Ireland (grant CTN 2014-012), the UPMC Learning While Doing Programme, the Breast Cancer Research Foundation , the French Ministry of Health (grant PHRC-20-0147), and the Minderoo Foundation; EU Patient-centric Clinical Trial Platforms has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 853966-2. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA , Children's Tumor Foundation, Global Alliance for TB Drug Development non-profit organization, and Springworks Therapeutics Inc. This publication reflects only the authors' views. The joint undertaking is not responsible for any use that may be made of the information it contains.
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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 | 0.326 | 0.431 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.025 | 0.020 |
| Insufficient payload (model declined to judge) | 0.063 | 0.017 |
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".