As the SARS-CoV-2 pandemic continues, little guidance is available on clinical indicators for safely discharging patients with severe COVID-19. To describe the clinical courses of adult patients admitted for COVID-19 and identify associations between inpatient clinical features and post-discharge need for acute care. Retrospective chart reviews were performed to record laboratory values, temperature, and oxygen requirements of 99 adult inpatients with COVID-19. Those variables were used to predict emergency department (ED) visit or readmission within 30 days post-discharge. Age ≥ 18 years, first hospitalization for COVID-19, admitted between March 1 and May 2, 2020, at University of California, Los Angeles (UCLA) Medical Center, managed by an inpatient medicine service. Ferritin, C-reactive protein, lactate dehydrogenase, D-dimer, procalcitonin, white blood cell count, absolute lymphocyte count, temperature, and oxygen requirement were noted. Of 99 patients, five required ED admission within 30 days, and another five required readmission. Fever within 24 h of discharge, oxygen requirement, and laboratory abnormalities were not associated with need for ED visit or readmission within 30 days of discharge after admission for COVID-19. Our data suggest that neither persistent fever, oxygen requirement, nor laboratory marker derangement was associated with need for acute care in the 30-day period after discharge for severe COVID-19. These findings suggest that physicians need not await the normalization of laboratory markers, resolution of fever, or discontinuation of oxygen prior to discharging a stable or improving patient with COVID-19.

Thank you for your commentary on “off-label” use of FRAX to better predict fracture risk for patients (such as those with diabetes or on high-risk medications) who don’t fit neatly into the FRAX algorithm. FRAX does allow for individualized calculation of relative risk of fracture across age

Fracture is a major cause of morbidity and death in postmenopausal women. Dual-energy x-ray absorptiometry (DXA) measures bone mineral density, which helps in estimating fracture risk and in identifying those who may benefit from treatment. Although screening guidelines differ somewhat for postmenopausal women under age 65, in general, DXA is indicated if the patient has a high risk for fracture.

Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable.

Before menopause, most simple cysts smaller than 5 cm resolve in 2 to 3 menstrual cycles and need no further intervention.

This article was migrated. The article was marked as recommended. IntroductionMedical educators need to demonstrate that their trainees meet expected competency levels when progressing through medical education. This study aimed to develop competency-based pass/fail cut-scores for a graduation required Objective Structured Clinical Examination (OSCE), and examine validity evidence for new standards.MethodsSix clinicians used the modified Angoff method to determine the cut-scores for an 8-station OSCE. The clinicians estimated the percentage of minimally competent students who would answer each checklist item correctly. Inter-rater reliability, differences in other academic achievements between pass/fail groups, educational impact, and response process were examined. ResultsOne hundred seventy-four rising 4th-year medical students participated in the OSCE. The cut-scores determined for the OSCE resulted in a substantially lower failure rate (5% vs. 29% of the previous year). The inter-rater reliability across domains and cases was .98 (95% CI = .97 - .99). The pass/fail groups significantly differed in six of the eight measures of academic achievements included in the study.DiscussionThe impact of the standards setting was substantial as it significantly reduced the failure rate and burdens of remediation for both students and faculty. The very high inter-rater reliability indicates that the modified Angoff method produced reliable cut-scores. The significant differences between the pass/fail groups in other measures support external validity of the standards and ensure no false passes. The study also supports response process validity by including discussion among judges and check of previous student performances, as well as recruiting and training multiple clinician educators experienced in medical student teaching.ConclusionFindings of the study provide strong evidence supporting validity of the new cut-scores from a wide spectrum of validity metrics, including response process, internal structure, relations to other variables, and consequences. The study also added to the literature the value of the modified Angoff method in determining competency-based standards for OSCEs.