The Overt Agitation Severity Scale for the objective rating of agitation

1997 ◽  
Vol 9 (4) ◽  
pp. 541-548 ◽  
2000 ◽  
Author(s):  
Gloria M. Miele ◽  
Kenneth M. Carpenter ◽  
Melissa Smith Cockerham ◽  
Kristin Dietz Trautman ◽  
Jack Blaine ◽  
...  

2005 ◽  
Author(s):  
James Westaby ◽  
Andrea Versenyi ◽  
Robert C. Hausmann

2019 ◽  
Author(s):  
Erik Forsell ◽  
Martin Kraepelien ◽  
Kerstin Blom ◽  
Nils Isacsson ◽  
Susanna Jernelöv ◽  
...  

2019 ◽  
Author(s):  
Yves Dauvilliers ◽  
Elisa Evangelista ◽  
Lucie Barateau ◽  
Regis Lopez ◽  
Sofiène Chenini ◽  
...  

Author(s):  
Christian Dorsch ◽  
Xiao Wang ◽  
Ferit Küçükay

AbstractThe calibration of conventional, hybrid and electric drivetrains is an important process during the development phase of any vehicle. Therefore, to optimize the comfort and dynamic behavior (known as driveability), many test drives are performed by experienced drivers during different driving maneuvers, e.g., launch, re-launch or gear shift. However, the process can be kept more consistent and independent of human-based deviations by using objective ratings. This study first introduces an objective rating system developed for the launch behavior of conventional vehicles with automatic transmission, dual-clutch transmission, and alternative drivetrains. Then, the launch behavior, namely comfort and dynamic quality, is compared between two conventional vehicles, a plug-in hybrid electric vehicle and a battery electric vehicle. Results show the benefits of pure electric drivetrains due to the lack of launch and shifting elements, as well as the usage of a highly dynamic electric motor. While the plug-in hybrid achieves a 10% higher overall rating compared to the baseline conventional vehicle, the pure electric vehicle even achieves a 21% higher overall rating. The results also highlight the optimization potential of battery electric vehicles regarding their comfort and dynamic characteristics. The transitions and the gradient of the acceleration build-up have a major influence on the launch quality.


2021 ◽  
pp. 088626052110152
Author(s):  
Ewa B. Stefanska ◽  
Nicholas Longpré ◽  
Rekayla S. Harriman

Stalking is a significant social issue. The inconsistency as to what defines stalking has resulted in the creation of different methods to measure the crime. However, there has been minimal work done that assesses the severity of individual stalking behaviors. The aim of the present study was to assess the level of stalking behavior in terms of severity within a randomly selected sample of 924 cases from the database of the National Stalking Helpline. Item response theory analyses were used to assist in developing a scale that displays the ranking order of each stalking behavior. These analyses were also used to examine whether the stalking behavioral items created a single continuum of severity of stalking. Results indicated that 16 stalking behavioral items of the 28 items present in the National Stalking Helpline, best represented the severity of stalking. Unwanted communication behaviors such as text messages and phone calls were located at the lower end of the severity scale, whereas criminal damage and death threats were mapped on the higher end of the continuum. The findings also revealed that the 16 items categorized under 6 factors. The findings of the present study provide many implications for stalking agency professionals and criminal justice responses.


Eye ◽  
2021 ◽  
Author(s):  
Lutfiah Al-Turk ◽  
James Wawrzynski ◽  
Su Wang ◽  
Paul Krause ◽  
George M. Saleh ◽  
...  

Abstract Background In diabetic retinopathy (DR) screening programmes feature-based grading guidelines are used by human graders. However, recent deep learning approaches have focused on end to end learning, based on labelled data at the whole image level. Most predictions from such software offer a direct grading output without information about the retinal features responsible for the grade. In this work, we demonstrate a feature based retinal image analysis system, which aims to support flexible grading and monitor progression. Methods The system was evaluated against images that had been graded according to two different grading systems; The International Clinical Diabetic Retinopathy and Diabetic Macular Oedema Severity Scale and the UK’s National Screening Committee guidelines. Results External evaluation on large datasets collected from three nations (Kenya, Saudi Arabia and China) was carried out. On a DR referable level, sensitivity did not vary significantly between different DR grading schemes (91.2–94.2.0%) and there were excellent specificity values above 93% in all image sets. More importantly, no cases of severe non-proliferative DR, proliferative DR or DMO were missed. Conclusions We demonstrate the potential of an AI feature-based DR grading system that is not constrained to any specific grading scheme.


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 7.2-7
Author(s):  
A. Santaniello ◽  
C. Bellocchi ◽  
L. Bettolini ◽  
M. Cassavia ◽  
G. Montanelli ◽  
...  

Background:The staging of interstitial lung disease (ILD) is important to monitor disease progression and for prognostication. A disease severity scale of Systemic Sclerosis (SSc)-related lung disease has long been proposed (i.e. Medsger’s severity scale). This scale was mostly developed by discussion and consensus and stage thresholds were not computed by a data-driven approach. Hidden Markov models (HMM) are methods to estimate population quantities for chronic diseases with a staged interpretation which are diagnosed by markers measured at irregular intervals.Objectives:To build a SSc-ILD specific disease severity scale with prognostic relevance via HMM modeling.Methods:A total of 358 SSc patients at risk for or with ILD were enrolled in a discovery (207 cases, Milan1) and in a validation (151 cases, Milan2, Pavia and Rome) cohort. Patients were included if satisfied the following criteria: 1) Diagnosis of SSc according to the EULAR/ACR 2013 criteria, 2) absence of anticentromere antibodies, 3) dcSSc subset or 4) other subsets with either 4a) ILD-related antibodies (Scl70, PmScl, Ku) or 4b) evidence of ILD on HRCT, 5) disease duration < 5 years at the time of the first pulmonary function test (PFT). Serial PFTs were retrieved and the time up to the last available visit -if the patient alive-, or to death due to pulmonary complications, was recorded. HMM were used to estimate the threshold of a 3-stage model (SL3SI, Scleroderma Lung 3-Stage Index) based on PFT functional values (normal/mild, moderate, severe involvement) in the discovery cohort. Survival estimates of the SL3SI model were compared to Medsger’s severity classes estimates and their predictive capability evaluated via the explained residual variation (R2) of prediction errors (the higher the better). One-hundred random replicates were generated to simulate the prediction effort in patients with different disease duration and lung severity.Results:Patients characteristics are summarized in the Table. Fifteen-years survival estimates for Mesdger’s classes in the discovery set were: normal=0.88, mild=0.86, moderate=0.84 and severe=0.71. The SL3SI was defined by the following thresholds: normal/mild, FVC and DLco >=75%; moderate FVC or DLco 74-55%; severe, FVC or DLco <55%. SL3SI 15-yrs survival estimates were: normal/mild=0.89, moderate=0.82 and severe=0.63. Prediction analysis showed a higher R2values at 15 yrs for the SL3SI compared to Medsger’s classes, providing evidence for a better predictive capability of the former (discovery: 0.31 vs 0.25; validation: 0.28 vs 0.19).Conclusion:The SL3SI, a simplified 3-stage functional model of SSc-ILD, yields better survival estimates and long-term prognostic information than Medsger’s classes. Its reproducibility and ease of use make it a useful tool for the functional and prognostic evaluation of SSc patients at risk for or with ILD.Table:VariablesDiscovery (n=207)Replication (n=151)DcSSc62 (30%)98 (64%)Age at first PFR48.6±1249.1±14.4Disease duration at first PFR1.7±1.61.3±2.4FVC90.5±18.191.1±20.2DLco70.7±19.861.3±20.1ILD on HRCT179 (86%)125 (80%)Scl70157 (76%)153 (78%)SSA63 (30%)32 (21%)n of visits38571473Follow-up time, yrs11±5.610.6±5.7Deaths27 (13%)23 (15%)Disclosure of Interests:Alessandro Santaniello: None declared, Chiara Bellocchi: None declared, Luca Bettolini: None declared, Marcello Cassavia: None declared, Gaia Montanelli: None declared, Adriana Severino: None declared, Monica Caronni: None declared, Corrado Campochiaro Speakers bureau: Novartis, Pfizer, Roche, GSK, SOBI, Enrico De Lorenzis: None declared, Gerlando Natalello: None declared, Paolo Delvino: None declared, Claudio Tirelli: None declared, Lorenzo Cavagna: None declared, Giacomo De Luca Speakers bureau: SOBI, Novartis, Celgene, Pfizer, MSD, Silvia Laura Bosello: None declared, Lorenzo Beretta Grant/research support from: Pfizer


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