METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.
RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.
CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.
METHODS: Blood lead level, anemia, hepatitis B virus (HBV) infection, tuberculosis infection or disease, and Strongyloides seropositivity data were available for 8148 refugee children (aged < 19 years) from Bhutan, Burma, Democratic Republic of Congo, Ethiopia, Iraq, and Somalia.
RESULTS: We identified distinct health profiles for each country of origin, as well as for Burmese children who arrived in the United States from Thailand compared with Burmese children who arrived from Malaysia. Hepatitis B was more prevalent among male children than female children and among children aged 5 years and older. The odds of HBV, tuberculosis, and Strongyloides decreased over the study period.
CONCLUSIONS: Medical screening remains an important part of health care for newly arrived refugee children in the United States, and disease risk varies by population.
METHODOLOGY: The scoping review will be carried out in six stages: (1) identifying the research question, (2) identifying relevant studies through electronic databases (i.e., PubMed, Scopus, Cochrane Reviews, Google Scholar, EBSCOHOST, Science Direct) and also gray literature, and (3) selection of studies to be included based on inclusion criteria. Search and initial screening of studies to be included will be conducted by two independent reviewers. Discrepancies will then be solved through discussion with other reviewers; (4) charting and categorizing extracted data in a pretested data extraction form; (5) collating, summarizing, and reporting the results; and lastly, (6) conducting consultation with stakeholders and experts in diabetes.
DISCUSSION: This scoping review protocol is aimed to provide a framework enabling us to map and summarize the findings from existing studies involving meal replacement. It will help researchers to identify the research gap and provide recommendations for future meal replacement studies. The results from this scoping review will be useful to various stakeholders in healthcare. It is also part of a research project in which the information obtained will be utilized in a clinical trial of a developed meal replacement plan. Dissemination of knowledge will also be done through presentations at related scientific conferences.