PedsCases content production handbook: A glimpse into the future of medical education

Introduction: PedsCases (http://www.pedscases.com) is a pediatric educational website for undergraduate medical students developed through student-faculty collaboration. PedsCases consists of pediatric specific clinical multiple-choice questions (MCQs), podcasts on key learning objectives, and interactive cases that teach students clinical decision-making skills. Our objective was to create a handbook for medical students that outlines how to create these types of high quality online learning resources. Handbook Production: The PedsCases editors and contributors worked together on this project and drew upon prior experiences and medical education theory to develop a handbook that carefully summarizes the core elements of writing clinical cases, MCQs, and podcasts. Individual members drafted a version of the handbook, which was then peer reviewed and verified by members of the PedsCases team, including the director of pediatric undergraduate education. Handbook Description: Each section consists of: 1) specific writing mechanics; 2) how to test knowledge application; and 3) tips specific for PedsCases content. The clinical case section explores how to develop and organize a clinical case scenario while ensuring that the content is applicable to medical students. The MCQ portion discusses how to properly write a question stem, challenging distracters, and how to integrate MCQs into clinical scenarios. The section on podcasts explains characteristics that make a podcast unique from lecture and discusses the advantages and disadvantages of podcasts as a learning venue. In addition, we review important considerations to take into account while creating medical education content specifically for undergraduate medical students. Conclusions: This handbook highlights how students can effectively transform textbook knowledge into an online resource that complements day-to-day medical education. Placing students in a virtual setting where they are able to make decisions, follow their curiosity, and arrive at the right conclusions, helps to develop well-rounded medical students and future medical educators

Development of a pediatric obstructive sleep apnea triage algorithm

Abstract Introduction Diagnosis and treatment of obstructive sleep apnea (OSA) in children is often delayed due to the high prevalence and limited physician and sleep testing resources. As a result, children may be referred to multiple specialties, such as pediatric sleep medicine and pediatric otolaryngology, resulting in long waitlists. Method We used data from our pediatric OSA clinic to identify predictors of tonsillectomy and/or adenoidectomy (AT). Before being seen in the clinic, parents completed the Pediatric Sleep Questionnaire (PSQ) and screening questionnaires for restless leg syndrome (RLS), nasal rhinitis, and gastroesophageal reflux disease (GERD). Tonsil size data were obtained from patient charts and graded using the Brodsky-five grade scale. Children completed an overnight oximetry study before being seen in the clinic, and a McGill oximetry score (MOS) was assigned based on the number and depth of oxygen desaturations. Logistic regression, controlling for otolaryngology physician, was used to identify significant predictors of AT. Three triage algorithms were subsequently generated based on the univariate and multivariate results to predict AT. Results From the OSA cohort, there were 469 eligible children (47% female, mean age = 8.19 years, SD = 3.59), with 89% of children reported snoring. Significant predictors of AT in univariate analysis included tonsil size and four PSQ questions, (1) struggles to breathe at night, (2) apneas, (3) daytime mouth breathing, and (4) AM dry mouth. The first triage algorithm, only using the four PSQ questions, had an odds ratio (OR) of 4.02 for predicting AT (sensitivity = 0.28, specificity = 0.91). Using only tonsil size, the second algorithm had an OR to predict AT of 9.11 (sensitivity = 0.72, specificity = 0.78). The third algorithm, where MOS was used to stratify risk for AT among those children with 2+ tonsils, had the same OR, sensitivity, and specificity as the tonsil-only algorithm. Conclusion Tonsil size was the strongest predictor of AT, while oximetry helped stratify individual risk for AT. We recommend that referral letters for snoring children include graded tonsil size to aid in the triage based on our findings. Children with 2+ tonsil sizes should be triaged to otolaryngology, while the remainder should be referred to a pediatric sleep specialist. Graphical abstract </jats:sec

Development of a pediatric obstructive sleep apnea triage algorithm

&lt;jats:title&gt;Abstract&lt;/jats:title&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Introduction&lt;/jats:title&gt; &lt;jats:p&gt;Diagnosis and treatment of obstructive sleep apnea (OSA) in children is often delayed due to the high prevalence and limited physician and sleep testing resources. As a result, children may be referred to multiple specialties, such as pediatric sleep medicine and pediatric otolaryngology, resulting in long waitlists.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Method&lt;/jats:title&gt; &lt;jats:p&gt;We used data from our pediatric OSA clinic to identify predictors of tonsillectomy and/or adenoidectomy (AT). Before being seen in the clinic, parents completed the Pediatric Sleep Questionnaire (PSQ) and screening questionnaires for restless leg syndrome (RLS), nasal rhinitis, and gastroesophageal reflux disease (GERD). Tonsil size data were obtained from patient charts and graded using the Brodsky-five grade scale. Children completed an overnight oximetry study before being seen in the clinic, and a McGill oximetry score (MOS) was assigned based on the number and depth of oxygen desaturations. Logistic regression, controlling for otolaryngology physician, was used to identify significant predictors of AT. Three triage algorithms were subsequently generated based on the univariate and multivariate results to predict AT.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Results&lt;/jats:title&gt; &lt;jats:p&gt;From the OSA cohort, there were 469 eligible children (47% female, mean age = 8.19 years, &lt;jats:italic&gt;SD&lt;/jats:italic&gt; = 3.59), with 89% of children reported snoring. Significant predictors of AT in univariate analysis included tonsil size and four PSQ questions, (1) struggles to breathe at night, (2) apneas, (3) daytime mouth breathing, and (4) AM dry mouth. The first triage algorithm, only using the four PSQ questions, had an odds ratio (OR) of 4.02 for predicting AT (sensitivity = 0.28, specificity = 0.91). Using only tonsil size, the second algorithm had an OR to predict AT of 9.11 (sensitivity = 0.72, specificity = 0.78). The third algorithm, where MOS was used to stratify risk for AT among those children with 2+ tonsils, had the same OR, sensitivity, and specificity as the tonsil-only algorithm.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Conclusion&lt;/jats:title&gt; &lt;jats:p&gt;Tonsil size was the strongest predictor of AT, while oximetry helped stratify individual risk for AT. We recommend that referral letters for snoring children include graded tonsil size to aid in the triage based on our findings. Children with 2+ tonsil sizes should be triaged to otolaryngology, while the remainder should be referred to a pediatric sleep specialist.&lt;/jats:p&gt; &lt;/jats:sec&gt;&lt;jats:sec&gt; &lt;jats:title&gt;Graphical abstract&lt;/jats:title&gt; &lt;/jats:sec&gt