Clinical research in the context of rapidly emerging public health threats
Bibliographic record
Abstract
A key lesson from a series of recent outbreaks of emerging pathogens of global public health importance including SARS-CoV, highly pathogenic avian influenza A (H5N1) virus, Nipah virus and the 2009 H1N1 virus pandemic was that mounting clinical research in response to a rapidly emerging infectious disease is extremely challenging and often delayed. During these events, important pathogenesis and clinical management data came mostly from sites that were already undertaking related clinical studies including those on inter-pandemic influenza (e.g., SEAICRN, Institute Pasteur Networks, Chinese University of Hong Kong, and University of Hong Kong) or from established national or regional research networks such as the Canadian and Australia/New Zealand Critical Care Trials Group. However there was very little cross-border coordination, unlike that which now exists in epidemiology and to an extent in virology and genomics. The clinical research response was cumbersome and slow despite years of global preparations for a potentially devastating influenza pandemic of avian origin or the next SARS-like outbreak. Although observational registries were mobilized, initiatives to launch randomized controlled trials or more sophisticated biologic studies generally missed the initial waves of the 2009 H1N1 pandemic and in many cases failed to enrol sufficient numbers of patients across the entire clinical spectrum of disease into studies, even during subsequent waves. During the 2009 H1N1 virus pandemic the efforts to prepare for a respiratory disease outbreak allowed a reasonably rapid and coordinated response on epidemiologic and diagnostic aspects of disease but failed in the timely conduct of clinical research aimed at improving patient management or understanding pathogenesis. The failure to have coordinated, comparable data on clinical management and pathogenesis of 2009 H1N1 virus infection meant that we missed the opportunity to improve patient outcomes. Indeed this has been a problem in almost all epidemics over the last decade (Nipah, SARS, H5N1, and in outbreaks of VHF) with very little research aimed at improving clinical management or understanding pathogenesis. This has demonstrated that unless something is done now to change the barriers faced in 2009, the next influenza (or other) outbreak will result in a similar missed opportunity to save lives. Furthermore, no integrated analyses exist of combined microbiological/virological, immunological, clinical, epidemiological, and genetic data for comprehensive assessment of host-emerging pathogen interactions that can inform prevention and control activities, and guide clinical management. There is an urgent need to establish a sustainable consortium of clinical research groups with broad geographic coverage (including low resource settings), cross-border coordination, commitment to open access, and capacity to conduct complementary high-quality, hospital-based pathogenesis and clinical management studies and the flexibility to respond immediately to rapidly emergent threats. We need a new paradigm for clinical research in the context of rapidly emerging public health threats and one appropriate to the sorts of challenges we will face in the 21st Century.
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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 | 0.094 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".