Reliability and accuracy of the Glasgow Coma Scale with experienced and inexperienced users

Rowley, Glenn; Fielding, Katy

doi:10.1016/0140-6736(91)91309-i

Cited by 249 publications

(177 citation statements)

References 9 publications

Supporting

Mentioning

153

Contrasting

Unclassified

Order By: Relevance

“…These vital signs are associated with adverse clinical outcomes and are measured periodically and frequently 6, 16, 26. Furthermore, they are objective values that are barely affected by medical staff measuring them 27. We defined the input vector as the predictor variables observed at the same time, and each input vector consisted of 4 vital signs: systolic blood pressure, HR, respiratory rate, and BT.…”

Section: Methodsmentioning

confidence: 99%

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Kwon

Lee²,

Lee³

et al. 2018

JAHA

234

205

View full text Add to dashboard Cite

BackgroundIn‐hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false‐alarm rates. We propose a deep learning–based early warning system that shows higher performance than the existing track‐and‐trigger systems.Methods and ResultsThis retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning–based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning–based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity.ConclusionsAn algorithm based on deep learning had high sensitivity and a low false‐alarm rate for detection of patients with cardiac arrest in the multicenter study.

show abstract

Section: Methodsmentioning

confidence: 99%

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Kwon

Lee²,

Lee³

et al. 2018

JAHA

234

205

View full text Add to dashboard Cite

show abstract

“…The GCS, which was developed, validated, and used widely to assess the level of consciousness and prognosis of patients with acute traumatic brain injuries (Rowley and Fielding, 1991) and non-traumatic causes of coma (Mullie et al , 1988), is insufficient for the assessment of vegetative state and minimally conscious state because of its crude measurement of awareness and its omission of relevant neurological functions (Howard and Hirsch, 1999).…”

Section: Glasgow Coma Scale (Gcs)mentioning

confidence: 99%

Electroencephalography (EEG) and Unconsciousness

Wu¹,

Yuan²

2012

Advances in Clinical Neurophysiology

View full text Add to dashboard Cite

“…뇌손상으로부터 회복된 정도를 의미하며 본 연구에서는 Rappaport Disability Rating Scale (DRS) (Rappaport, Hall, Hopkins, Belleza, & Cope, 1982 (Bahloul et al, 2004;Hukkelhoven et al, 2005;Mosenthal et al, 2004;Rovlias & Kotsou, 2004 (Bakay & Ward, 1983;Jagger, Jane, & Rimel, 1983;Jennett & Teasdale, 1977) 측정자 사이의 신뢰도 또한 양호한 것으로(r=0.86-0.95) 제시되었다 (Rowley & Fielding, 1991;Stanczak, White, & Gouview, 1984). …”

Section: ) 회복 정도unclassified

Influence of Age, Gender, and Severity on Recovery of Patients with Brain Injury

Seo

2008

J Korean Acad Nurs

View full text Add to dashboard Cite

Purpose: This study was conducted to investigate the individual and cross influences of age, gender, and severity on recovery of patients with brain injury. Methods: For the purpose of the study, traumatic or spontaneous brain injury patients admitted to the intensive care unit (ICU) were conveniently selected. The data regarding outcomes were collected 3 months after admission. Results: Individual influences of the study variables on patients' recovery were significant, except for gender. But while the individual influence of gender on recovery was not significant, cross influence of gender and age was significant, but only for the sub-dimension of 'arousalbility and awareness'. The study results also showed that 3-way cross influence of gender, age, and severity was only significant on the sub-dimension of 'arousalbility and awareness'. Conclusion: The sub-dimension of recovery cross influenced by the demographic factors of gender and age, and severity was 'arousalbility and awareness'. This might indicate that the study variables that cross influencing recovery had more influence on consciousness compared to physical function and psycho-social adaptation.

show abstract

Reliability and accuracy of the Glasgow Coma Scale with experienced and inexperienced users

Cited by 249 publications

References 9 publications

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Electroencephalography (EEG) and Unconsciousness

Influence of Age, Gender, and Severity on Recovery of Patients with Brain Injury

Contact Info

Product

Resources

About