Proceedings of the 17th Coordination Meeting of the WHO Radiation Emergency Medical Preparedness and Assistance Network—13-15 September, 2023—Seoul, Republic of Korea
Notice bibliographique
Résumé
In an era of intensifying geopolitical instability, technological risk, and environmental disruption, the threat of radiological and nuclear emergencies looms large over public health systems worldwide.The 17th Coordination Meeting of the World Health Organization's Radiation Emergency Medical Preparedness and Assistance Network (REMPAN), held in Seoul, Republic of Korea, from 13 to 15 September 2023, convened global experts, institutions, and policymakers to confront this challenge head-on.This special issue of Disaster Medicine and Public Health Preparedness captures the breadth and depth of those proceedings, presenting 20 manuscripts that collectively advance the science, strategy, and solidarity required to protect populations from radiological harm.The meeting kindly hosted by the Korea Institute of Radiological and Medical Sciences (KIRAMS) underscored the critical importance of national preparedness frameworks, exemplified by Korea's National Radiological Emergency Medical System.KIRAMS has pioneered innovations in training, infrastructure, and biological dosimetry-integrating virtual reality tools, automated scoring systems, and high-throughput assays to enhance emergency response capabilities.Their work demonstrates how technical excellence and international collaboration can translate into scalable, life-saving interventions.Several manuscripts explore the evolving landscape of radiation epidemiology, particularly in the context of low-dose exposure.The Korean Radiation Workers Study encompassing over 200,000 individuals, offers a robust model for integrating occupational dose data with national health records.Complementary studies on aircrews and residents near nuclear facilities reflect growing public concern and scientific inquiry into the long-term health effects of low-dose radiation.These efforts are vital for informing risk communication, regulatory policy, and ethical decision-making.The legacy of the Fukushima Daiichi Nuclear Power Plant accident remains a focal point for both technical and community-based recovery.Multiple contributions detail the phased revitalization of affected areas, emphasizing the role of transparent communication, intergenerational knowledge transfer, and international scientific exchange.These lessons are not only historicalthey are foundational for building resilient, informed, and empowered communities in the face of future disasters.Clinical management of radiation injuries and internal contamination is another cornerstone of this issue.The collaboration between EURADOS and REMPAN on wound contamination protocols, the development of WHO's evidence-based guidelines for actinide exposure, and the European mass casualty preparedness framework all reflect a growing consensus: medical response must be multidisciplinary, standardized, and scalable.From cytogenetic assays to decontamination tents, from decision guides to simulation-based training, the tools of disaster medicine are evolving rapidly-and must be disseminated widely.The geopolitical context of radiological preparedness is also addressed with urgency.Ukraine's health sector, under constant threat of nuclear escalation, has received critical support from the WHO in the form of equipment, training, and strategic evaluation.Moldova's refugeeresponsive hospital preparedness initiative offers a replicable model for integrating radiological emergency planning into broader humanitarian response systems.These case studies remind us that radiological threats do not exist in isolation-they intersect with war, displacement, and global inequities.Finally, the editorial spotlight must fall on the role of harmonization and health diplomacy.The work of ISO Working Group 18 on retrospective dosimetry, the ICRP's expanded protection framework, and other partners and stakeholders all contribute to a shared Disaster Medicine and Public Health Preparedness www.cambridge.
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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,088 | 0,021 |
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 ».