Communication between healthcare professionals and deaf patients has been particularly challenging during the COVID-19 pandemic. We have explored the possibility to automatically translate phrases that are frequently used in the diagnosis and treatment of hospital patients, in particular phrases related to COVID-19, from Dutch or English to Dutch Sign Language (NGT). The prototype system we developed displays translations either by means of pre-recorded videos featuring a deaf human signer (for a limited number of sentences) or by means of animations featuring a computer-generated signing avatar (for a larger, though still restricted number of sentences). We evaluated the comprehensibility of the signing avatar, as compared to the human signer. We found that, while individual signs are recognized correctly when signed by the avatar almost as frequently as when signed by a human, sentence comprehension rates and clarity scores for the avatar are substantially lower than for the human signer. We identify a number of concrete limitations of the JASigning avatar engine that underlies our system. Namely, the engine currently does not offer sufficient control over mouth shapes, the relative speed and intensity of signs in a sentence (prosody), and transitions between signs. These limitations need to be overcome in future work for the engine to become usable in practice.
The overlap in symptoms between joint bleeds and flare‐ups of haemophilia arthropathy (HA) creates difficulties in differentiating between the two conditions. Diagnosis of haemarthrosis is currently empirically made based upon clinical presentations. However, no standard diagnostic criteria are available. To offer appropriate treatment, rapid and accurate diagnosis is essential. Additionally, adequate differentiation can decrease health costs significantly. Aim The aim of this study was to identify signs and symptoms to differentiate between an intra‐articular joint bleed and an acute flare‐up of HA in patients with haemophilia and make an initial proposal of items to include in a diagnostic criteria set. Methods Six focus group interviews with a total of 13 patients and 15 professionals were carried out. The focus groups were structured following the Nominal Group Technique (NGT). Results The most important signs and symptoms used to differentiate between joint bleeds and HA were (i) course of the symptoms, (ii) cause of the complaints, (iii) joint history, (iv) type of pain and (v) degree of impairments in range of motion. Conclusion This qualitative study provides insight into signs and symptoms that are currently used to differentiate between joint bleeds and flare‐ups of HA. Results of this study can be used to develop a valid and standardized clinical diagnostic criteria set to differentiate between these two conditions. Further research is necessary to validate the signs and symptoms found in this study.
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ABSTRACT It is unknown whether heterogeneity in effects of self-management interventions in patients with chronic obstructive pulmonary disease (COPD) can be explained by differences in programme characteristics. This study aimed to identify which characteristics of COPD self-management interventions are most effective. Systematic search in electronic databases identified randomised trials on self-management interventions conducted between 1985 and 2013. Individual patient data were requested for meta-analysis by generalised mixed effects models. 14 randomised trials were included (67% of eligible), representing 3282 patients (75% of eligible). Univariable analyses showed favourable effects on some outcomes for more planned contacts and longer duration of interventions, interventions with peer contact, without log keeping, without problem solving, and without support allocation. After adjusting for other programme characteristics in multivariable analyses, only the effects of duration on all-cause hospitalisation remained. Each month increase in intervention duration reduced risk of all-cause hospitalisation (time to event hazard ratios 0.98, 95% CI 0.97–0.99; risk ratio (RR) after 6 months follow-up 0.96, 95% CI 0.92–0.99; RR after 12 months follow-up 0.98, 95% CI 0.96–1.00). Our results showed that longer duration of self-management interventions conferred a reduction in allcause hospitalisations in COPD patients. Other characteristics are not consistently associated with differential effects of self-management interventions across clinically relevant outcomes.
