An Emotional Agent for Moral Impairment Rehabilitation in TBI Patients

The ability to identify the emotions of others is a key component of what is known as social cognition. Narratives exploit this mechanism to create an emotional bond with the characters and to maintain the engagement of the audience throughout the story. In this paper, we illustrate a case study in emotion understanding in stories that exploits a computational agent to explore emotion impairment in a group of traumatic brain injured people. The study focuses on moral emotions, aiming to investigate the differences in moral functioning that characterize traumatic brain injured patients. After comparing the understanding of the moral and emotional facets of the agent's behavior in traumatic brain injured patients and in neurologically intact controls, slight–yet meaningful–differences were observed between the two groups. We describe the test methodology and results, highlighting their implications for the design of rehabilitation applications based on virtual agents

Blindness and glaucoma: A multicenter data review from 7 academic eye clinics

Purpose To evaluate frequency, conversion rate, and risk factors for blindness in glaucoma patients treated in European Universities. Methods This multicenter retrospective study included 2402 consecutive patients with glaucoma in at least one eye. Medical charts were inspected and patients were divided into those blind and the remainder (''controls''). Blindness was defined as visual acuity-0.05 and/or visual field loss to less than 10°. Results Unilateral and bilateral blindness were respectively 11.0% and 1.6%at the beginning, and 15.5%and 3.6% at the end of the observation period (7.5±5.5 years, range:1-25 years)//conversion to blindness (at least unilateral) was 1.1%/year. 134 eyes (97 patients) developed blindness by POAG during the study. At the first access to study centre, they had mean deviation (MD) of -17.1±8.3 dB and treated intraocular pressure (IOP) of 17.1±6.6 mmHg. During follow-up the IOP decreased by 14%in these eyes but MD deteriorated by 1.1±3.5 dB/year, which was 5-fold higher than controls (0.2±1.6 dB/year). In a multivariate model, the best predictors for blindness by glaucoma were initial MD (p<0.001), initial IOP (p<0.001), older age at the beginning of follow-up (p<0.001), whereas final IOP was found to be protective (p<0.05). Conclusions In this series of patients, blindness occurred in about 20%. Blindness by glaucoma had 2 characteristics: late diagnosis and/or late referral, and progression of the disease despite in most cases IOP was within the range of normality and target IOP was achieved//it could be predicted by high initial MD, high initial IOP, and old age

Causes of blindness in the study.

<p>ARMD, age-related macular disease; BVO, branch vein occlusion; CVO, central vein occlusion; PACG, primary angle-closure glaucoma; POAG, primary open-angle glaucoma.</p><p>Causes of blindness in the study.</p

Multivariate linear model fitted with Final MD values as dependent variable and Final IOP as predictor, together with the same predictors as in Table 5.

<p>*p<0.1</p><p>**p<0.05</p><p>***p<0.01</p><p>IOP, intraocular pressure; MD, mean deviation</p><p>(except for the Initial IOP).</p

Characteristics of IOP in patients developing blindness by POAG in the course of the study.

<p>IOP, intraocular pressure (mean of the two eyes).</p><p>Characteristics of IOP in patients developing blindness by POAG in the course of the study.</p

Table shows a multivariate linear model fitted with Final MD values as dependent variable and Initial IOP as predictor, together with other predictors.

<p>*p<0.1</p><p>**p<0.05</p><p>***p<0.01</p><p>IOP, intraocular pressure; MD, mean deviation</p><p>Table shows a multivariate linear model fitted with Final MD values as dependent variable and Initial IOP as predictor, together with other predictors.</p

Hazard ratios and confidence intervals for each risk factor in determining blindness.

<p>IOP, intraocular pressure; MD, Mean Deviation; Final, at the end of follow up. Since the model used is a parametric accelerated failure model with a Weibull distribution, the hazard ratio depends on the scale parameter, which is different for each center due to stratification. The presented hazard ratios are a global calculation (i.e. with no strata) although the significance of the covariates was assessed with a stratified model.</p><p>Hazard ratios and confidence intervals for each risk factor in determining blindness.</p