1,043 research outputs found

    Final report on heat transfer study

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    Commissioning of the NED cryostat

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    Coercion and Effect

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    EFFECTS OF PISCINE REOVIRUS INFECTION ON INNATE IMMUNE SIGNALLING IN SALMON

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    Reducing Medical Admissions into Hospital through Optimising Medicines (REMAIN HOME) Study: Protocol for a stepped wedge, cluster randomised trial

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    Introduction: A model of general practitioner (GP) and pharmacist collaboration in primary care may be an effective strategy to reduce medication-related problems and provide better support to patients after discharge. The aim of this study is to investigate whether a model of structured pharmacist and GP care reduces hospital readmissions in patients at risk of readmission. Methods and analysis: This protocol details a stepped wedge cluster randomised trial that will recruit participants over nine months with a 12 month follow up. There will be 14 clusters each representing a different general practice medical centre. A total of 2240 participants will be recruited from hospital who attend an enrolled medical centre, take five or more long-term medicines or whose reason for admission was related to heart failure or chronic obstructive pulmonary disease. The intervention is a multi-faceted service, involving a pharmacist integrated into a medical centre to assist patients after hospitalisation. Participants will meet with the practice pharmacist and their GP after discharge to review and reconcile their medicines and discuss changes made in hospital. The pharmacist will follow up with the participant and liaise with other health professionals involved in the participant’s care. The control will be usual care, which usually involves a patient self-organising a visit to their GP after hospital discharge. The primary outcome is the rate of unplanned, all-cause hospital readmissions over 12 months which will be analysed using a mixed effects Poisson regression model with a random effect for cluster and a fixed effect to account for any temporal trend. A cost analysis will be undertaken to compare the healthcare costs associated with the intervention to those of usual care

    Introducing first-year medical students to surgery via a hands-on surgical workshop

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    INTRODUCTION Surgical interest among medical students has declined in recent years. Lack of exposure to basic procedural skills and formulated perceptions of surgeons prior to surgical clerkships increases discomfort felt among medical students, deterring students from entering the field of surgery. METHODS In 2021 we instituted a hand-on "Introduction to Surgical Techniques" course to first-year medical students at Harvard Medical School. The course was provided prior to clinical rotations and focused on the fundamentals of knot-tying and suturing. Students were exposed to a breadth of surgical instructors. A survey was offered to all students before and after the workshop. RESULTS A total of 112 first-year medical students (of 124) participated in the pre and post-workshop surveys for a response rate of 90.3%. Most students (86.1%) were very satisfied with the suturing course and all of the students’ abilities to perform a two-handed knot and running suture significantly improved (p< 0.001; p< 0.001). 26.0% of students demonstrated an increased likelihood to enter the surgical field after completing the workshop. Underrepresented racial and ethnical minorities responded well to the workshop with 40% of Hispanics displaying an increased likelihood to enter the surgical field after the workshop. 34.4% of women highly rated their likelihood to consider surgery in the future. CONCLUSION The results of this study demonstrate early exposure of surgical training and surgical mentors during medical students’ pre-clinical years improves suturing techniques as well as increases the likelihood students will go into a surgical field in the future

    Automated Monitoring of Online News Streams: Topic Detection and Tracking Considerations

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    This paper describes the term frequency patterns found in online news summaries published over a seven-week period. The patterns are analyzed qualitatively and quantitatively to facilitate the refinement of algorithms used for the automatic detection and tracking of important topics appearing in streams of text. It is shown that a term's importance cannot be measured in raw frequency counts or significant increases in volume alone. The impact of these findings on existing algorithms is discussed, and new approaches for automated story detection and presentation are considered
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