METHODS: Segmented and validated wheeze sounds was collected from 55 asthmatic patients from the trachea and lower lung base (LLB) during tidal breathing maneuvers. Segmented wheeze sounds have been grouped in to nine datasets based on auscultation location, breath phases and a combination of phase and location. Frequency based features F25, F50, F75, F90, F99 and mean frequency (MF) were calculated from normalized power spectrum. Subsequently, multivariate analysis was performed.
RESULTS: Generally frequency features observe statistical significance (p < 0.05) for the majority of datasets to differentiate severity level Ʌ = 0.432-0.939, F(12, 196-1534) = 2.731-11.196, p < 0.05, ɳ2 = 0.061-0.568. It was observed that selected features performed better (higher effect size) for trachea related samples Ʌ = 0.432-0.620, F(12, 196-498) = 6.575-11.196, p < 0.05, ɳ2 = 0.386-0.568.
CONCLUSIONS: The results demonstrated dthat severity levels of asthmatic patients with tidal breathing can be identified through computerized wheeze sound analysis. In general, auscultation location and breath phases produce wheeze sounds with different characteristics.
METHODS: We analysed 350 items used in 7 professional examinations and determined their distractor efficiency and the number of functional distractors per item. The items were sorted into five groups - excellent, good, fair, remediable and discarded based on their discrimination index. We studied how the distractor efficiency and functional distractors per item correlated with these five groups.
RESULTS: Correlation of distractor efficiency with psychometric indices was significant but far from perfect. The excellent group topped in distractor efficiency in 3 tests, the good group in one test, the remediable group equalled excellent group in one test, and the discarded group topped in 2 tests.
CONCLUSIONS: The distractor efficiency did not correlate in a consistent pattern with the discrimination index. Fifty per cent or higher distractor efficiency, not hundred percent, was found to be the optimum.
METHODS: This study was done as part of the continuous professional development for Health Education England West Midlands speciality trainees in diabetes and Endocrinology. Standardized transcripts of anonymized real-life endocrinology (endocrine session) and diabetes cases (diabetes session) were used in the simulation model. Trainees interacted with moderators through WhatsApp® in this model. All cases were then discussed in detail by a consultant endocrinologist with reference to local, national and international guidelines. Trainee acceptance rate and improvement in their self-reported confidence levels post-simulation were assessed.
RESULTS: 70.8% (n = 17/24) and 75% (n = 18/24) strongly agreed the simulation session accommodated their personal learning style and the session was engaging. 66.7% (n = 16/24) strongly felt that the simulation was worth their time. In the endocrine session, there was a significant improvement in trainees' confidence in the management of craniopharyngioma (p = 0.0179) and acromegaly (p = 0.0025). There was a trend towards improved confidence levels to manage Cushing's disease and macroprolactinoma. In diabetes session, there was a significant improvement in trainees' confidence to interpret continuous glucose monitor readings (p = 0.01). There was a trend towards improvement for managing monogenic diabetes, hypoglycaemic unawareness and interpreting Libre readings. Overall, there was a significant improvement in trainees' confidence in managing cases that were discussed post-simulation.
CONCLUSION: SIMBA is an effective learning model to improve trainees' confidence to manage various diabetes and endocrine case scenarios. More sessions with a variety of other speciality case scenarios are needed to further assess SIMBA's effectiveness and application in other areas of medical training.
Methods: We expanded a prior framework based on Grading of Evidence, Assessment, Development and Evaluation (GRADE) to include GER. The revised framework is applied systematically during the formulation of research questions and comprises: (1) assessment of the GRADE strength and quality rating of recommendations; (2) mandatory inclusion of research questions identified from a global stakeholder survey; and (3) selection of the GER standards and principles most relevant to the question through discussion and consensus. For each question, we articulated: (1) the most appropriate and robust study design; (2) an alternative pragmatic design if the ideal design was not feasible; and (3) the methodological challenges facing researchers through identifying potential biases.
Results: We identified 39 research questions, 7 overarching research approaches and 13 discrete feasible study designs. Availability and accessibility were most frequently identified as the GER standards and principles to consider when planning studies, followed by privacy and confidentiality. Selection and detection bias were the primary methodological challenges across mixed methods, quantitative and qualitative studies. A lack of generalisability potentially limits the use of study results with non-participation in research potentially highest in more vulnerable populations.
Conclusion: A framework based on GRADE that includes stakeholders' values and identification of core GER standards and principles provides a practical, systematic approach to identifying research questions from a WHO guideline. Clear guidance for future studies will contribute to an anticipated 'living guidelines' approach within WHO. Foregrounding GER as a separate component of the framework is innovative but further elaboration to operationalise appropriate indicators for SRHR self-care interventions is required.
METHOD: This paper is motivated by the gap in the literature, thus proposes an algorithm that measures the strength of the significant features that contribute to heart disease prediction. The study is aimed at predicting heart disease based on the scores of significant features using Weighted Associative Rule Mining.
RESULTS: A set of important feature scores and rules were identified in diagnosing heart disease and cardiologists were consulted to confirm the validity of these rules. The experiments performed on the UCI open dataset, widely used for heart disease research yielded the highest confidence score of 98% in predicting heart disease.
CONCLUSION: This study managed to provide a significant contribution in computing the strength scores with significant predictors in heart disease prediction. From the evaluation results, we obtained important rules and achieved highest confidence score by utilizing the computed strength scores of significant predictors on Weighted Associative Rule Mining in predicting heart disease.
METHODS AND ANALYSIS: A three-phase approach to validate content for curriculum guidelines on AMR will be adopted. First, literature review and content analysis were conducted to find out the available pertinent literature in dentistry programmes. A total of 23 potential literature have been chosen for inclusion within this study following literature review and analysis in phase 1. The materials found will be used to draft curriculum on antimicrobials for dentistry programmes. The next phase involves the validation of the drafted curriculum content by recruiting local and foreign experts via a survey questionnaire. Finally, Delphi technique will be conducted to obtain consensus on the important or controversial modifications to the revised curriculum.
ETHICS AND DISSEMINATION: An ethics application is currently under review with the Institute of Health Science Research Ethics Committee, Universiti Brunei Darussalam. All participants are required to provide a written consent form. Findings will be used to identify significant knowledge gaps on AMR aspect in a way that results in lasting change in clinical practice. Moreover, AMR content priorities related to dentistry clinical practice will be determined in order to develop need-based educational resource on microbes, hygiene and prudent antimicrobial use for dentistry programmes.