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ردیف | عنوان | نوع |
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1 |
Resident Opioid Prescribing Habits Do Not Reflect Best Practices in Post-Operative Pain Management: An Assessment of the Knowledge and Education Gap
عادت های تجویز داروهای ساکن، بهترین شیوه ها را در مدیریت درد پس از عمل منعکس نمی کنند: ارزیابی شکاف دانش و آموزش-2021 OBJECTIVE: To evaluate deficiencies in knowledge and education in opioid prescribing and to compare surgical resident
opioid-prescribing practices to Opioid Prescribing Engagement Network (OPEN) procedure-specific guidelines.
DESIGN: Anonymous web-based survey distributed to all general surgery residents to evaluate prior education received and confidence in knowledge in opioid prescribing. The number of 5 milligram oxycodone tablets prescribed for common procedures was assessed and compared with OPEN for significance using Wilcoxon signed rank tests. SETTING: General surgery residency program within large university-based tertiary medical center. PARTICIPANTS: Categorical general surgery residents of all postgraduate years. RESULTS: Fifty-six of 72 (78%) categorical residents completed the survey. Few reported receiving formal education in opioid prescribing in medical school (32%) or residency (16%). While 82% of residents felt confident in opioid side effects, fewer felt the same with regards to opioid pharmacokinetics (36%) or proper opioid disposal (29%). Opioids prescribed varied widely with residents prescribing significantly more than recommended by OPEN in 9 of 14 procedures. CONCLUSIONS: Tackling the evolving opioid epidemic requires a multidisciplinary approach that addresses prescribing at all steps of the process, starting with trainee education. KEY WORDS: Opioid Epidemic | Opioid Prescribing Engagement Network | Surgical Education | Resident Education COMPETENCIES: Patient Care, Medical Knowledge, Practice-Based Learning and Improvement |
مقاله انگلیسی |
2 |
Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation
هوش مصنوعی در آموزش پزشکی: بهترین روش هایی که با استفاده از یادگیری ماشینی برای ارزیابی تخصص جراحی در شبیه سازی واقعیت مجازی انجام می شود-2019 OBJECTIVE: Virtual reality simulators track all movements
and forces of simulated instruments, generating enormous
datasets which can be further analyzed with machine learning
algorithms. These advancements may increase the
understanding, assessment and training of psychomotor
performance. Consequently, the application of machine
learning techniques to evaluate performance on virtual reality
simulators has led to an increase in the volume and complexity
of publications which bridge the fields of computer
science, medicine, and education. Although all disciplines
stand to gain from research in this field, important differences
in reporting exist, limiting interdisciplinary communication
and knowledge transfer. Thus, our objective was to
develop a checklist to provide a general framework when
reporting or analyzing studies involving virtual reality surgical
simulation and machine learning algorithms. By including
a total score as well as clear subsections of the
checklist, authors and reviewers can both easily assess the
overall quality and specific deficiencies of a manuscript.
DESIGN: The Machine Learning to Assess Surgical Expertise
(MLASE) checklist was developed to help computer science,
medicine, and education researchers ensure quality
when producing and reviewing virtual reality manuscripts
involving machine learning to assess surgical expertise.
SETTING: This study was carried out at the McGill Neurosurgical
Simulation and Artificial Intelligence Learning Centre.
PARTICIPANTS: The authors applied the checklist to 12
articles using machine learning to assess surgical expertise
in virtual reality simulation, obtained through a systematic
literature review.
RESULTS: Important differences in reporting were found
between medical and computer science journals. The
medical journals proved stronger in discussion quality
and weaker in areas related to study design. The opposite
trends were observed in computer science journals.
CONCLUSIONS: This checklist will aid in narrowing the
knowledge divide between computer science, medicine,
and education: helping facilitate the burgeoning field of
machine learning assisted surgical education. ( J Surg Ed
000:110. 2019 Association of Program Directors in
Surgery. Published by Elsevier Inc. All rights reserved.) KEY WORDS: simulation| surgery | education | artificial intelligence | assessment | machine learning |
مقاله انگلیسی |