American journal of disaster medicine · 2013
Gleaning data from disaster: a hospital-based data mining method to study all-hazard triage after a chemical disaster.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Craig Jean B, Culley Joan M, Tavakoli Abbas S, Svendsen Erik R
Аннотация
OBJECTIVE: To describe the methods of evaluating currently available triage models for their efficacy in appropriately triaging the surge of patients after an all-hazards disaster.
DESIGN: A method was developed for evaluating currently available triage models using extracted data from medical records of the victims from the Graniteville chlorine disaster.
SETTING: On January 6, 2005, a freight train carrying three tanker cars of liquid chlorine was inadvertently switched onto an industrial spur in central Graniteville, SC. The train then crashed into a parked locomotive and derailed. This caused one of the chlorine tankers to rupture and immediately release ~60 tons of chlorine. Chlorine gas infiltrated the town with a population of 7,000.
PARTICIPANTS: This research focuses on the victims who received emergency care in South Carolina.
RESULTS: With our data mapping and decision tree logic, the authors were successful in using the available extracted clinical data to estimate triage categories for use in our study.
CONCLUSIONS: The methodology outlined in this article shows the potential use of well-designed secondary analysis methods to improve mass casualty research. The steps are reliable and repeatable and can easily be extended or applied to other disaster datasets.
DESIGN: A method was developed for evaluating currently available triage models using extracted data from medical records of the victims from the Graniteville chlorine disaster.
SETTING: On January 6, 2005, a freight train carrying three tanker cars of liquid chlorine was inadvertently switched onto an industrial spur in central Graniteville, SC. The train then crashed into a parked locomotive and derailed. This caused one of the chlorine tankers to rupture and immediately release ~60 tons of chlorine. Chlorine gas infiltrated the town with a population of 7,000.
PARTICIPANTS: This research focuses on the victims who received emergency care in South Carolina.
RESULTS: With our data mapping and decision tree logic, the authors were successful in using the available extracted clinical data to estimate triage categories for use in our study.
CONCLUSIONS: The methodology outlined in this article shows the potential use of well-designed secondary analysis methods to improve mass casualty research. The steps are reliable and repeatable and can easily be extended or applied to other disaster datasets.