| P50 | author | Robert D. Gibbons | Q46996511 |
| P2093 | author name string | Seo Young Park | |
| | Matthew M Churpek | |
| | Dana P Edelson | |
| | Trevor C Yuen | |
| P2860 | cites work | A prediction rule to identify low-risk patients with community-acquired pneumonia | Q28301165 |
| | APACHE II: a severity of disease classification system | Q29547729 |
| | ViEWS--Towards a national early warning score for detecting adult inpatient deterioration | Q34126489 |
| | Early detection of impending physiologic deterioration among patients who are not in intensive care: Development of predictive models using data from an automated electronic medical record | Q34207928 |
| | Prognostic signs and the role of operative management in acute pancreatitis | Q34219633 |
| | Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial | Q34427380 |
| | Incidence of treated cardiac arrest in hospitalized patients in the United States. | Q35387134 |
| | Predicting cardiac arrest on the wards: a nested case-control study | Q35929920 |
| | Derivation of a cardiac arrest prediction model using ward vital signs* | Q36044394 |
| | Review and performance evaluation of aggregate weighted 'track and trigger' systems. | Q37077027 |
| | A review, and performance evaluation, of single-parameter "track and trigger" systems. | Q37213886 |
| | Why don't hospital staff activate the rapid response system (RRS)? How frequently is it needed and can the process be improved? | Q37865595 |
| | Improving in-hospital cardiac arrest process and outcomes with performance debriefing | Q38390014 |
| | Clinical deterioration in the condition of patients with acute medical illness in Australian hospitals: improving detection and response | Q39742287 |
| | Calculating early warning scores--a classroom comparison of pen and paper and hand-held computer methods. | Q39760566 |
| | The use of combined physiological parameters in the early recognition of the deteriorating acute medical patient. | Q39810708 |
| | The identification of risk factors for cardiac arrest and formulation of activation criteria to alert a medical emergency team | Q42039925 |
| | Anticipating events of in-hospital cardiac arrest | Q48881299 |
| | Validation of an abbreviated Vitalpac™ Early Warning Score (ViEWS) in 75,419 consecutive admissions to a Canadian Regional Hospital | Q50787422 |
| | Waiting for organ transplantation: results of an analysis by an Institute of Medicine Committee | Q51835333 |
| | Internal validation of predictive models: efficiency of some procedures for logistic regression analysis | Q52933297 |
| | Can physiological variables and early warning scoring systems allow early recognition of the deteriorating surgical patient?* | Q64458691 |
| | Empirical comparisons of proportional hazards and logistic regression models | Q69620777 |
| | Reproducibility of physiological track-and-trigger warning systems for identifying at-risk patients on the ward | Q79603548 |
| | The objective medical emergency team activation criteria: a case-control study | Q79620805 |
| | Longitudinal analysis of one million vital signs in patients in an academic medical center | Q84560462 |
| | Better ViEWS ahead? It is high time to improve patient safety by standardizing Early Warning Scores | Q84575044 |
| P433 | issue | 4 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | electronic health records | Q10871684 |
| P304 | page(s) | 841-848 | |
| P577 | publication date | 2014-04-01 | |
| P1433 | published in | Critical Care Medicine | Q5186605 |
| P1476 | title | Using electronic health record data to develop and validate a prediction model for adverse outcomes in the wards*. | |
| P478 | volume | 42 | |