Global health on the front lines: an innovative medical student elective combining education and service during the COVID-19 pandemic

Global health on the front lines: an innovative medical student elective combining education and service during the COVID-19 pandemic

An revolutionary medical scholar elective mixed student-directed, faculty-supported on-line studying with COVID-19 response subject placements. This research evaluated college students’ expertise within the course, the curriculum content material and format, and its short-term influence on college students’ information and attitudes round COVID-19. College students responded to dialogue board prompts all through the course and submitted pre-/post-course reflections. Pre-/post-course questionnaires assessed pandemic information and attitudes utilizing 4-point Likert scales.

Authors collected mixture knowledge on enrollment, dialogue posts, subject placements, and scholarly work ensuing from course actions. After the elective, authors performed a spotlight group with a comfort pattern of 6 members. Institutional elective analysis knowledge was included in evaluation. Authors analyzed questionnaire knowledge with abstract statistics and paired t-tests evaluating information and attitudes earlier than and after the elective. Reflection items, dialogue posts, and focus group knowledge have been analyzed utilizing content material evaluation with a phenomenological method.

Twenty-seven college students enrolled. Every scholar posted a mean of two.Four authentic dialogue posts and three.1 responses. Imply information rating elevated from 43.Eight to 60.8% (p < 0.001) between pre- and post-course questionnaires. Information self-assessment additionally elevated (2.Four vs. 3.5 on Likert scale, p < 0.0001), and college students reported elevated engagement within the pandemic response (2.7 vs. 3.6, p < 0.0001). College students reported elevated fluency in discussing the pandemic and elevated appreciation for the subject of public well being.

There was no distinction in college students’ degree of hysteria concerning the pandemic after course participation (3.Zero vs. 3.1, p = 0.53). Twelve college students (44.4%) accomplished the institutional analysis. All rated the course “superb” or “wonderful.” College students favorably reviewed the subject placements, instructed readings, self-directed analysis, and studying from friends. They instructed extra clearly outlined expectations and improved stability between volunteer and academic hours. The elective was well-received by college students, achieved acknowledged aims, and garnered public consideration.

Course management ought to monitor college students’ time dedication intently in service-learning settings to make sure acceptable stability of service and training. Scholar engagement in a catastrophe response is inadequate to deal with nervousness associated to the catastrophe; future course iterations ought to embrace a deal with self-care throughout occasions of disaster. This instructional innovation may function a mannequin for medical faculties globally. It has beforehand been proven that knowledge mining can enhance the prediction and diagnostic precision of sort 2 diabetes mellitus.

Assessing opioid overdose threat: A assessment of scientific prediction fashions using patient-level knowledge


Drug, and particularly opioid-associated, overdoses stay a significant public well being drawback in the US. A number of research have examined particular person threat components related to overdose threat, however analysis creating scientific threat prediction instruments for overdose has solely emerged in the previous couple of years. We performed a complete assessment of the literature on patient-level components related to opioid-associated overdose threat, with an emphasis on scientific threat prediction fashions for opioid-associated overdose in the US. Research that developed and/or validated scientific prediction fashions have been intently reviewed and evaluated to find out the state of the subject.

We recognized 12 research that reported threat prediction fashions for opioid-associated overdose threat. Printed fashions have been developed from a wide range of knowledge sources, together with Veterans Well being Administration knowledge, Medicare knowledge, business insurance coverage knowledge, and statewide linked datasets. Research reported mannequin efficiency utilizing measures of discrimination, normally at good-to-excellent ranges, although they didn’t all the time assess calibration. C-statistics have been higher for fashions that included scientific predictors (c-statistics: 0.75-0.95) in comparison with fashions with out them (c-statistics: 0.69-0.82).

Exterior validation of fashions was uncommon, and we discovered no research evaluating implementation of fashions or threat prediction instruments into scientific follow. A typical characteristic of those fashions was a excessive price of false positives, largely as a result of opioid-associated overdose is uncommon within the common inhabitants. Thus, efforts to implement prediction fashions into follow ought to take into consideration that revealed fashions overestimate overdose threat for a lot of low-risk sufferers.

Future prediction fashions assessing overdose threat ought to make use of exterior validation and handle mannequin calibration. With a view to translate findings from prediction fashions into scientific public well being profit, future research ought to deal with creating scientific prediction instruments primarily based on prediction fashions, implementing these instruments into scientific follow, and evaluating the influence of those fashions on therapy selections, affected person outcomes, and, finally, opioid overdose charges.

Global health on the front lines: an innovative medical student elective combining education and service during the COVID-19 pandemic

Potential worth and influence of knowledge mining and machine studying in scientific diagnostics

Knowledge mining includes the usage of mathematical sciences, statistics, synthetic intelligence, and machine studying to find out the relationships between variables from a big pattern of knowledge. A number of research have utilized machine studying to evaluate hypertension and metabolic syndrome-associated biomarkers, in addition to refine the evaluation of heart problems threat. Machine studying strategies have additionally been utilized to evaluate new biomarkers and survival outcomes in sufferers with renal ailments to foretell the event of continual kidney illness, illness development, and renal graft survival.

Within the latter, random forest strategies have been discovered to be the very best for the prediction of continual kidney illness. Some research have investigated the prognosis of nonalcoholic fatty liver illness and acute liver failure, in addition to remedy response prediction in sufferers with viral issues, utilizing determination tree fashions. Machine studying methods, akin to Sparse Excessive-Order Interplay Mannequin with Rejection Possibility, have been used for diagnosing Alzheimer’s illness.