METHODS: A two-round online survey will be conducted, followed by a stakeholder consensus meeting to identify a Core Domain Set. Participants will belong to one of two stakeholder groups: healthcare users with lived experience of tinnitus, and professionals with relevant clinical, commercial, or research experience.
DISCUSSION: This study will establish a Core Domain Set for the evaluation of electrical stimulation-based interventions for tinnitus via an e-Delphi study. The resulting Core Domain Set will act as a minimum standard for reporting in future clinical trials of electrical stimulation interventions for tinnitus. Standardisation will facilitate comparability of research findings.
MATERIALS AND METHODS: A cross-sectional study was conducted among the healthcare workers in the paediatric department at three public specialist hospitals in Negeri Sembilan between 15 and 21 April 2022. Data were collected through a self-administered questionnaire.
RESULTS: Out of the 504 eligible healthcare workers, 493 participated in this study (response rate 97.8%). The overall prevalence of COVID-19 (11 March 2020-15 April 2022) among healthcare workers was 50.9%. The majority (80.1%) were infected during the Omicron wave two months before the survey. Household contacts accounted for 35.9% of infection sources. The proportion of non-doctors in the COVID-19-infected group was significantly higher compared to the non-infected group (74.1% vs 64.0%, p=0.016). The COVID-19-infected group had a higher proportion of schoolgoing children (44.6% vs 30.6%, p=0.001) and children who attended pre-school/sent to the babysitter (49.0% vs 24.4%, p<0.001). There were no significant differences between infection rates among the healthcare workers working in the tertiary hospital and the district hospitals. There were also no significant differences in the proportion of COVID-19- infected doctors and nurses when analysed by seniority.
CONCLUSION: Our study provided an estimate on the prevalence of COVID-19 among paediatric healthcare workers in Negeri Sembilan and the factors associated with infection, which captures the extent and magnitude of this pandemic on the state's paediatric department. Most infections resulted from household contact, with a higher proportion of infected healthcare workers having young children.
METHODS: A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles.
RESULTS: A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5-10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009-0.790]% for the PC cohort and 0.051 [0.030-0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation.
CONCLUSION: VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.
METHODS: The International Society of Global Health (ISoGH) used the Child Health and Nutrition Research Initiative (CHNRI) method to identify research priorities for future pandemic preparedness. Eighty experts in global health, translational and clinical research identified 163 research ideas, of which 42 experts then scored based on five pre-defined criteria. We calculated intermediate criterion-specific scores and overall research priority scores from the mean of individual scores for each research idea. We used a bootstrap (n = 1000) to compute the 95% confidence intervals.
RESULTS: Key priorities included strengthening health systems, rapid vaccine and treatment production, improving international cooperation, and enhancing surveillance efficiency. Other priorities included learning from the coronavirus disease 2019 (COVID-19) pandemic, managing supply chains, identifying planning gaps, and promoting equitable interventions. We compared this CHNRI-based outcome with the 14 research priorities generated and ranked by ChatGPT, encountering both striking similarities and clear differences.
CONCLUSIONS: Priority setting processes based on human crowdsourcing - such as the CHNRI method - and the output provided by ChatGPT are both valuable, as they complement and strengthen each other. The priorities identified by ChatGPT were more grounded in theory, while those identified by CHNRI were guided by recent practical experiences. Addressing these priorities, along with improvements in health planning, equitable community-based interventions, and the capacity of primary health care, is vital for better pandemic preparedness and response in many settings.
METHODS: This study was a narrative review using literature in the last 10 years identified by web-based search on PubMed and Scopus using keywords. A total of 33 articles that were closely related to the field and application in dentistry were included. The methodology, main results, and future research recommendations, if applicable, were extracted and reviewed.
RESULTS: The authors in this study had identified several areas such as orofacial pain and pain control research, dental anxiety, dental education, oral healthcare perceptions and access, living with dental diseases and dental treatment experience in which the phenomenological method was used to gain an in-depth understanding of the topic.
CONCLUSIONS: There are several advantages of using the phenomenological research method, such as the small sample size needed, the diverse and unique perspective that can be obtained and the ability to improve current understanding, especially from the first-person perspective.