Abstract The medical evaluation is an important part of the clinical and legal process when child sexual abuse is suspected. Practitioners who examine children need to be up to date on current ...recommendations regarding when, how, and by whom these evaluations should be conducted, as well as how the medical findings should be interpreted. A previously published article on guidelines for medical care for sexually abused children has been widely used by physicians, nurses, and nurse practitioners to inform practice guidelines in this field. Since 2007, when the article was published, new research has suggested changes in some of the guidelines and in the table that lists medical and laboratory findings in children evaluated for suspected sexual abuse and suggests how these findings should be interpreted with respect to sexual abuse. A group of specialists in child abuse pediatrics met in person and via online communication from 2011 through 2014 to review published research as well as recommendations from the Centers for Disease Control and Prevention and the American Academy of Pediatrics and to reach consensus on if and how the guidelines and approach to interpretation table should be updated. The revisions are based, when possible, on data from well-designed, unbiased studies published in high-ranking, peer-reviewed, scientific journals that were reviewed and vetted by the authors. When such studies were not available, recommendations were based on expert consensus.
...patients who underwent chemotherapy or surgery in the past month had a numerically higher risk (three 75% of four patients) of clinically severe events than did those not receiving chemotherapy or ...surgery (six 43% of 14 patients; figure). ...stronger personal protection provisions should be made for patients with cancer or cancer survivors. ...more intensive surveillance or treatment should be considered when patients with cancer are infected with SARS-CoV-2, especially in older patients or those with other comorbidities.
Elevated lipoprotein(a) (Lpa) and family history (FHx) of coronary heart disease (CHD) are individually associated with cardiovascular risk, and Lp(a) is commonly measured in those with FHx.
The aim ...of this study was to determine independent and joint associations of Lp(a) and FHx with atherosclerotic cardiovascular disease (ASCVD) and CHD among asymptomatic subjects.
Plasma Lp(a) was measured and FHx was ascertained in 2 cohorts. Elevated Lp(a) was defined as the highest race-specific quintile. Independent and joint associations of Lp(a) and FHx with cardiovascular risk were determined using Cox regression models adjusted for cardiovascular risk factors.
Among 12,149 ARIC (Atherosclerosis Risk In Communities) participants (54 years, 56% women, 23% black, 44% with FHx), 3,114 ASCVD events were observed during 21 years of follow-up. FHx and elevated Lp(a) were independently associated with ASCVD (hazard ratio HR: 1.17; 95% confidence interval CI: 1.09 to 1.26, and HR: 1.25; 95% CI: 1.12 to 1.40, respectively), and no Lp(a)-by-FHx interaction was noted (p = 0.75). Compared with subjects without FHx and nonelevated Lp(a), those with either elevated Lp(a) or FHx were at a higher ASCVD risk, while those with both had the highest risk (HR: 1.43; 95% CI: 1.27 to 1.62). Similar findings were observed for CHD risk in ARIC, in analyses stratified by premature FHx, and in an independent cohort, the DHS (Dallas Heart Study). Presence of both elevated Lp(a) and FHx resulted in greater improvement in ASCVD and CHD risk reclassification and discrimination indexes than either marker alone.
Elevated plasma Lp(a) and FHx have independent and additive joint associations with cardiovascular risk and may be useful concurrently for guiding primary prevention therapy decisions.
We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.
Four years of data were ...extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycemic episodes (BG ≤3.9 and ≤2.9 mmol/L, respectively). We used patient demographics, administered medications, vital signs, laboratory results, and procedures performed during the hospital stays to inform the model. Two iterations of the data set included the doses of insulin administered and the past history of inpatient hypoglycemia. Eighteen different prediction models were compared using the area under the receiver operating characteristic curve (AUROC) through a 10-fold cross validation.
We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, and metformin), and albumin levels. The machine learning model with the best performance was the XGBoost model (AUROC 0.96). This outperformed the logistic regression model, which had an AUROC of 0.75 for the estimation of the risk of clinically significant hypoglycemia.
Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.
A virtual patient (VP) can be a useful tool to foster the development of medical history-taking skills without the inherent constraints of the bedside setting. Although VPs hold the promise of ...contributing to the development of students' skills, documenting and assessing skills acquired through a VP is a challenge.
We propose a framework for the automated assessment of medical history taking within a VP software and then test this framework by comparing VP scores with the judgment of 10 clinician-educators (CEs).
We built upon 4 domains of medical history taking to be assessed (breadth, depth, logical sequence, and interviewing technique), adapting these to be implemented into a specific VP environment. A total of 10 CEs watched the screen recordings of 3 students to assess their performance first globally and then for each of the 4 domains.
The scores provided by the VPs were slightly higher but comparable with those given by the CEs for global performance and for depth, logical sequence, and interviewing technique. For breadth, the VP scores were higher for 2 of the 3 students compared with the CE scores.
Findings suggest that the VP assessment gives results akin to those that would be generated by CEs. Developing a model for what constitutes good history-taking performance in specific contexts may provide insights into how CEs generally think about assessment.
There is no "gold standard" for assessing disease activity in patients with eosinophilic esophagitis (EoE). We aimed to compare physicians' judgment of EoE activity with patients' judgment of symptom ...severity. We also aimed to examine the relative contribution of symptoms as well as endoscopic and histologic findings in shaping physicians' judgment of EoE activity.
Six gastroenterologists (all EoE experts) assessed EoE-associated symptoms in adult patients. Patients completed a symptom instrument and provided global assessment of EoE symptom severity (PatGA) (Likert scale: 0 (inactive) to 10 (most active)). Following esophagogastroduodenoscopy with biopsy sampling, gastroenterologists provided a global assessment of EoE activity (PhysGA) (Likert scale from 0 to 10) based on patient history and endoscopic and histologic findings. Linear regression and analysis of variance was used to quantify the extent to which variations in severity of EoE symptoms and endoscopic and histologic findings explain variations in PhysGA.
A total of 149 EoE patients were prospectively included (71.8% male, median age at inclusion 38 years, 71.8% with concomitant allergies). A moderate positive correlation between PhysGA and PatGA (rho=0.442, P<0.001) was observed and the mean difference in the Bland-Altman plot was 1.77. Variations in severity of endoscopic findings, symptoms, and histologic findings alone explained 53%, 49%, and 30%, of the variability in PhysGA, respectively. Together, these findings explained 75% of variability in PhysGA.
Gastroenterologists rate EoE activity mainly on the basis of endoscopic findings and symptoms and, to a lesser extent, on histologic findings.
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