Objective: Due to the multidimensional nature of resonance disorders, multivariate diagnostic assessment is advisable. The nasality severity index (NSI) is based on this point of view. Because of the influence of personal and environmental variables on the current NSI, this study aims to refine this index. Design: Prospective case-control study. Setting: Tertiary university hospital. Patients: Forty-two patients with cleft lip and palate and 50 children without resonance disorders were tested. Interventions: Resonance was investigated by perceptual as well as objective measurements. A Nasometer was used to score nasalance, and spectral speech characteristics of a sustained sound /i:/ were determined, among which the voice low tone to high tone ratio (VLHR). Binary logistic regression analysis was performed to calculate the optimal index to discriminate patients from control children. Additionally, the validity of the index was determined based on data from an independent patient and control group. Results: The NSI 2.0, a weighted linear combination of three variables, can be obtained using the equation NSI 2.0 = 13.20 − (.0824 × nasalance /u:/ [%]) − (.260 × nasalance oral text [%]) − (.242 × VLHR 4.47*F0 [dB]). The NSI has a sensitivity of 92% and a specificity of 100%. Moreover, it has excellent validity (sensitivity 88%, specificity 89%). Conclusions: The NSI 2.0 discriminates patients from control children with high sensitivity, specificity, and validity. This multiparametric method can offer a more powerful approach in the assessment and treatment planning of individuals with hypernasality.
Set up: Focus on ethical agency, of which ethical sensitivity is a part Best practice unit (i.e. a community of practice with inquisitive objectives): cooperation of 12 social workers (of 6 different welfare organizations) and 2 researchers (UAS teachers) Practice based co-research: involving social workers as reflective and inquisitive professionals with regard to their own practice Phenomenological design: focus on (interpreting) experiences
The aim of this study was to assess the predictive ability of the frailty phenotype (FP), Groningen Frailty Indicator (GFI), Tilburg Frailty Indicator (TFI) and frailty index (FI) for the outcomes mortality, hospitalization and increase in dependency in (instrumental) activities of daily living ((I)ADL) among older persons. This prospective cohort study with 2-year follow-up included 2420 Dutch community-dwelling older people (65+, mean age 76.3±6.6 years, 39.5% male) who were pre-frail or frail according to the FP. Mortality data were obtained from Statistics Netherlands. All other data were self-reported. Area under the receiver operating characteristic curves (AUC) was calculated for each frailty instrument and outcome measure. The prevalence of frailty, sensitivity and specifcity were calculated using cutoff values proposed by the developers and cutoff values one above and one below the proposed ones (0.05 for FI). All frailty instruments poorly predicted mortality, hospitalization and (I)ADL dependency (AUCs between 0.62–0.65, 0.59–0.63 and 0.60–0.64, respectively). Prevalence estimates of frailty in this population varied between 22.2% (FP) and 64.8% (TFI). The FP and FI showed higher levels of specifcity, whereas sensitivity was higher for the GFI and TFI. Using a different cutoff point considerably changed the prevalence, sensitivity and specifcity. In conclusion, the predictive ability of the FP, GFI, TFI and FI was poor for all outcomes in a population of pre-frail and frail community-dwelling older people. The FP and the FI showed higher values of specifcity, whereas sensitivity was higher for the GFI and TFI.
Wildlife crime is an important driver of biodiversity loss and disrupts the social and economic activities of local communities. During the last decade, poaching of charismatic megafauna, such as elephant and rhino, has increased strongly, driving these species to the brink of extinction. Early detection of poachers will strengthen the necessary law enforcement of park rangers in their battle against poaching. Internationally, innovative, high tech solutions are sought after to prevent poaching, such as wireless sensor networks where animals function as sensors. Movement of individuals of widely abundant, non-threatened wildlife species, for example, can be remotely monitored ‘real time’ using GPS-sensors. Deviations in movement of these species can be used to indicate the presence of poachers and prevent poaching. However, the discriminative power of the present movement sensor networks is limited. Recent advancements in biosensors led to the development of instruments that can remotely measure animal behaviour and physiology. These biosensors contribute to the sensitivity and specificity of such early warning system. Moreover, miniaturization and low cost production of sensors have increased the possibilities to measure multiple animals in a herd at the same time. Incorporating data about within-herd spatial position, group size and group composition will improve the successful detection of poachers. Our objective is to develop a wireless network of multiple sensors for sensing alarm responses of ungulate herds to prevent poaching of rhinos and elephants.
