Notice bibliographique
Résumé
To the Editor The current Ebola epidemic, the largest in history, has to date affected primarily African nations. However, several cases were diagnosed in the United States, including 3 involving health care workers.1 The spread of Ebola to North America and the resulting risk to health care workers bear striking similarities to the 2003 outbreak of severe acute respiratory syndrome (SARS), which originated in Taipei, Taiwan, and quickly spread to other countries. In Toronto, Canada, the epidemic resulted in 438 probable or suspected cases of SARS. More than half of these cases involved health care workers, including 3 of the 43 SARS-related deaths in Toronto.2 Indeed, more than half of SARS cases in both Toronto and Taiwan were infections related to the provision of health care.2,3 In particular, critical care providers and anesthesiologists, especially those involved in airway procedures such as endotracheal intubation, were at the highest risk of infection because the primary mode of transmission was through contact of respiratory droplets with mucous membranes.2 A detailed review of the experiences of health care workers showed that frontline workers directly involved in patient care had limited opportunity to rapidly inform policy makers about their concerns and suggestions. Indeed, in the face of an acute and evolving epidemic, the serious practical challenges of developing and modifying guidelines cannot be underestimated. During the SARS epidemic, safety protocols were typically developed by infection control experts who lacked clinical expertise dealing with critically ill patients or by clinicians who lacked experience treating patients with SARS.2 Communication was inconsistent and confusing, and information came from multiple sources, including local hospitals and national and international agencies. Through this letter, we wish to share the lessons we learned in hopes of helping others avoid similar mistakes during the current Ebola epidemic. As reported in a 2006 article, we identified all frontline health care workers who had performed intubation with SARS-infected patients.2 Of the 59 health care workers who had performed at least 1 such intubation, 33 consented to an interview. Within this group, 3 (13%) of the 23 health care workers who performed intubation during the first wave of SARS (February 23 to April 21, 2003) became infected, whereas none of the 10 health care workers who performed intubation during the second wave (from April 22 to July 1, 2003) became infected. This striking reduction in incidence presumably resulted from increased use of isolation precautions and implementation of simple and practical management guidelines, proposed at least, in part, by frontline health care workers. For example, it was recommended that droplet spread could be minimized by using a paralytic agent for intubation. In addition, it was recommended that intubation should be performed by the most experienced health care workers available, to reduce the time and number of attempts required. Early airway intervention was beneficial, and close proximity of airway tools was necessary, with personnel immediately available to assist.2Table 1: Infectious Disease Risk Management FrameworkThese descriptions of personal experiences were used to develop a risk management framework that could rapidly integrate the experiences of health care workers into guidelines and recommendations for use during future outbreaks of infectious diseases. The analysis showed potential areas of weakness or vulnerability, termed “breakpoints,” in processes, people, tools, and infrastructure. Using the grounded theory approach and the data from our interviews with frontline health care workers, we identified recommendations to mitigate the spread of future disease outbreaks. The risk management framework, shown in Table 1, is highly relevant to the current Ebola epidemic, and it is our hope that the lessons learned during the SARS epidemic a decade ago will not be forgotten. In advance of a major outbreak, bidirectional communication systems must be established and risk management tools adopted to limit the spread of Ebola to health care workers and others. Karen C. Nanji, MD, MPH Department of Anesthesia, Critical Care and Pain Medicine Massachusetts General Hospital Department of Anaesthesia Harvard Medical School Boston, Massachusetts [email protected] Beverley A. Orser, MD, PhD Department of Anesthesia Sunnybrook Health Sciences Center Toronto, Ontario, Canada Department of Anesthesia University of Toronto Toronto, Ontario, Canada
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».