Insights of SEDRIC, the Surveillance and Epidemiology of Drug-Resistant Infections Consortium
Bibliographic record
Abstract
The increasing threat from infection with drug-resistant pathogens is among the most serious public health challenges of our time. Formed by Wellcome in 2018, the Surveillance and Epidemiology of Drug-Resistant Infections Consortium (SEDRIC) is an international think tank whose aim is to inform policy and change the way countries track, share, and analyse data relating to drug-resistant infections, by defining knowledge gaps and identifying barriers to the delivery of global surveillance. SEDRIC delivers its aims through discussions and analyses by world-leading scientists that result in recommendations and advocacy to Wellcome and others. As a result, SEDRIC has made key contributions in furthering global and national actions. Here, we look back at the work of the consortium between 2018-2024, highlighting notable successes. We provide specific examples where technical analyses and recommendations have helped to inform policy and funding priorities that will have real-world impact on the surveillance and epidemiology of infections with drug-resistant pathogens.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".