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Showing results (31-40 of 39) with videos related to

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Scientific Reports|January 13, 2023
Pulmonary function three to five months after hospital discharge for COVID-19: a single centre cohort studyTina Krueger, Janelle van den Heuvel, Vivian van Kampen-van den Boogaart, et al.
Acta Anaesthesiologica Scandinavica|October 8, 2021
Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortalityDrago Plečko, Nicolas Bennett, Johan Mårtensson, et al.
International Journal of Medical Informatics|September 26, 2022
Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health recordsIacopo Vagliano, Martijn C Schut, Ameen Abu-Hanna, et al.
Critical Care Explorations|October 21, 2021
Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress SyndromeTariq A Dam, Harm-Jan de Grooth, Thomas Klausch, et al.
Annals of Intensive Care|October 20, 2022
Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learningTariq A Dam, Luca F Roggeveen, Fuda van Diggelen, et al.
Shock (Augusta, Ga.)|September 26, 2022
INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDYJasper M Smit, Lotte Exterkate, Arne J van Tienhoven, et al.
Critical Care (London, England)|August 24, 2021
The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patientsLucas M Fleuren, Tariq A Dam, Michele Tonutti, et al.
Intensive Care Medicine Experimental|June 28, 2021
Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data WarehouseLucas M Fleuren, Michele Tonutti, Daan P de Bruin, et al.
Critical Care (London, England)|December 28, 2021
Predictors for extubation failure in COVID-19 patients using a machine learning approachLucas M Fleuren, Tariq A Dam, Michele Tonutti, et al.
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Showing results (31-40 of 39) with videos related to

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Pageof 4
You have reached the last page of results.This site can display upto 39 results.
Scientific Reports|January 13, 2023
Pulmonary function three to five months after hospital discharge for COVID-19: a single centre cohort studyTina Krueger, Janelle van den Heuvel, Vivian van Kampen-van den Boogaart, et al.
Acta Anaesthesiologica Scandinavica|October 8, 2021
Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortalityDrago Plečko, Nicolas Bennett, Johan Mårtensson, et al.
International Journal of Medical Informatics|September 26, 2022
Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health recordsIacopo Vagliano, Martijn C Schut, Ameen Abu-Hanna, et al.
Critical Care Explorations|October 21, 2021
Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress SyndromeTariq A Dam, Harm-Jan de Grooth, Thomas Klausch, et al.
Annals of Intensive Care|October 20, 2022
Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learningTariq A Dam, Luca F Roggeveen, Fuda van Diggelen, et al.
Shock (Augusta, Ga.)|September 26, 2022
INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDYJasper M Smit, Lotte Exterkate, Arne J van Tienhoven, et al.
Critical Care (London, England)|August 24, 2021
The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patientsLucas M Fleuren, Tariq A Dam, Michele Tonutti, et al.
Intensive Care Medicine Experimental|June 28, 2021
Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data WarehouseLucas M Fleuren, Michele Tonutti, Daan P de Bruin, et al.
Critical Care (London, England)|December 28, 2021
Predictors for extubation failure in COVID-19 patients using a machine learning approachLucas M Fleuren, Tariq A Dam, Michele Tonutti, et al.
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