MATERIALS AND METHODS: An electronic search was performed in PubMed, SCOPUS, and Web of Science using a combination of relevant keywords: digital workflow, digital designing, computer-assisted design-computer aided manufacturing, 3D printing, maxillectomy, and mandibulectomy. The Joanna Briggs Institute Critical Appraisal Tool was used to assess the quality of evidence in the studies reviewed.
RESULTS: From a total of 542 references, 33 articles were selected, including 25 on maxillary prostheses and 8 on mandibular prostheses. The use of digital workflows was limited to one or two steps of the fabrication of the prostheses, and only four studies described a complete digital workflow. The most preferred method for data acquisition was intraoral scanning with or without a cone beam computed tomography combination.
CONCLUSION: Currently, the fabrication process of maxillofacial prostheses requires combining digital and conventional methods. Simplifying the data acquisition methods and providing user-friendly and affordable software may encourage clinicians to use the digital workflow more frequently for patients requiring maxillofacial prostheses.
METHODS: ARCHERY is a non-randomised prospective study to evaluate the quality and economic impact of AI-based automated radiotherapy treatment planning for cervical, head and neck, and prostate cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 990 patients (330 for each cancer type) has been calculated based on an estimated 95% treatment plan acceptability rate. Time and cost savings will be analysed as secondary outcome measures using the time-driven activity-based costing model. The 48-month study will take place in six public sector cancer hospitals in India (n=2), Jordan (n=1), Malaysia (n=1) and South Africa (n=2) to support implementation of the software in LMICs.
ETHICS AND DISSEMINATION: The study has received ethical approval from University College London (UCL) and each of the six study sites. If the study objectives are met, the AI-based software will be offered as a not-for-profit web service to public sector state hospitals in LMICs to support expansion of high quality radiotherapy capacity, improving access to and affordability of this key modality of cancer cure and control. Public and policy engagement plans will involve patients as key partners.
METHODS: A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated.
RESULTS: The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy.
CONCLUSIONS: Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.
Methods: A cross-sectional study was conducted with 500 nurses, selected through multistage cluster sampling, from the hospitals in Shiraz in 2017. The data collection tools were the Siberia Schering's Emotional Intelligence Standard Questionnaire and the Hospital Job Stress Standard Questionnaire, completed through the self-report method. The data were analysed using SPSS 22 software.
Results: The mean scores of emotional intelligence and job stress were 113.59 ± 14.70 (total score = 165) and 97.10 ± 14.27 (total score = 175), respectively. The correlation test showed that there was an inverse relationship between emotional intelligence and job stress (r = -0.474, P < 0.001). Also, the multiple linear regression analysis showed that self-awareness, social consciousness, and income predicted 25% of the job stress in the subjects under study (r2 = 0.25).
Conclusion: Regarding the relatively strong and inverse relationship between the nurses' emotional intelligence and job stress, it is suggested that emotional intelligence workshops be included in the in-service training programs of the nurses.