Clinical research in the context of rapidly emerging public health threats
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,094 | 0,165 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,005 | 0,009 |
| Communication savante | 0,016 | 0,014 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,012 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».