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To develop an angular correction methodology and characterize a high-resolution complementary metal-oxide-semiconductor (CMOS) array for patient specific quality assurance on a robotic arm linear accelerator. Beam path files from the treatment planning software (TPS) were used to calculate the angle of radiation beam with respect to the detector plane. Beams from multiple discrete angles were delivered to the CMOS detector array and an angular dependency look up table (LUT) was created. The LUT was then used to correct for the angular dependency of the detector. An iso-centric 5 mm fixed cone, non iso-centric multi-target fixed cone, 10 mm Iris and a multi-leaf collimator (MLC) based collimated plan were delivered to the phantom and compared to the TPS with and without angular correction applied. Additionally, the CMOS array was compared to gafchromic film and a diode array. Large errors of up to 30% were observed for oblique angles. When angular correction was applied, the gamma passing rate increased from 99.2% to 100% (average gamma value decreased from 0.29 to 0.14) for the 5-mm iso-centric cone plan. Similarly, the passing rate increased from 84.0% to 100% for the Iris plan and from 49.98% to 98.4% for the MLC plan when angular correction was applied. For the multi-target plan, applying angular correction improved the gamma passing rate from 94% to 99.6%. The 5 mm iso-centric fixed cone plan was also delivered to film, and the gamma passing rate was 91.3% when using gafchromic film as the reference dataset, whereas the diode array provided insufficient sampling for this plan. A methodology of calculating the beam angle based on the beam path files was developed and validated. The array was demonstrated to be superior to other quality assurance tools because of its sub-millimeter spatial resolution and immediate read out of the results.

The prenatal care visit structure has changed little over the past century despite the rapid evolution of technology including Internet and mobile phones. Little is known about how pregnant women engage with technologies and the interface between these tools and medical care, especially for women of lower socioeconomic status. We sought to understand how women use technology during pregnancy through a qualitative study with women enrolled in the Women, Infants, and Children (WIC) program. We recruited pregnant women ages 18 and older who owned a smartphone, at a WIC clinic in central Pennsylvania. The focus group guide included questions about women's current pregnancy, their sources of information, and whether they used technology for pregnancy-related information. Sessions were audiotaped and transcribed. Three members of the research team independently analyzed each transcript, using a thematic analysis approach. Themes related to the topics discussed were identified, for which there was full agreement. Four focus groups were conducted with a total of 17 women. Three major themes emerged as follows. First, the prenatal visit structure is not patient-centered, with the first visit perceived as occurring too late and with too few visits early in pregnancy when women have the most questions for their prenatal care providers. Unfortunately, the educational materials women received during prenatal care were viewed as unhelpful. Second, women turn to technology (eg, Google, smartphone applications) to fill their knowledge gaps. Turning to technology was viewed to be a generational approach. Finally, women reported that technology, although frequently used, has limitations. The results of this qualitative research suggest that the current prenatal care visit structure is not patient-centered in that it does not allow women to seek advice when they want it most. A generational shift seems to have occurred, resulting in pregnant women in our study turning to the Internet and smartphones to fill this gap, which requires significant skills to navigate for useful information. Future steps may include developing interventions to help health care providers assist patients early in pregnancy to seek the information they want and to become better consumers of Internet-based pregnancy resources.

Rural primary care providers report increasing rates of professional burnout, which can further exacerbate rural provider shortages and health disparities. From 2023 to 2025, the Project ECHO team at Penn State University developed and delivered an educational rural health telementoring program, collaboratively with stakeholders, to disseminate guideline-concordant care to rural primary care clinicians. The program focused on key rural topics and created a professional learning community aimed at decreasing participant burnout. Self-reported results of the pilot program's participants ( = 106) demonstrate increased knowledge ( < .001) and reduced professional isolation. Future programing will expand data collection to explore longer-term impact.

With the recent CyberKnife treatment planning system (TPS) upgrade from Precision 1.0 to Precision 2.0, the new VOLO optimizer was released for plan optimization. The VOLO optimizer sought to overcome some of the limitations seen with the Sequential optimizer from previous TPS versions. The purpose of this study was to investigate the clinical impact of the VOLO optimizer on treatment plan quality and clinical treatment efficiency as compared to the Sequential optimizer. Treatment plan quality was evaluated in four categories of patients: Brain Simple (BS), Brain Complex (BC), Spine Complex (SC), and Prostate (PC). A total of 60 treatment plans were compared using both the Sequential and VOLO optimizers with Iris and MLC collimation with the same clinical constraints. Metrics evaluated included estimated treatment time, monitor units (MUs) delivered, conformity index (CI), and gradient index (GI). Furthermore, the clinical impact of the VOLO optimizer was evaluated through statistical analysis of the patient population treated during the 4 months before (n = 297) and 4 months after (n = 285) VOLO introduction. Significant MU and time reductions were observed for all four categories planned. MU reduction ranged from −14% (BS Iris) to −52% (BC MLC), and time reduction ranged from −11% (BS Iris) to −22% (BC MLC). The statistical analysis of patient population before and after VOLO introduction for patients using 6D Skull tracking with fixed cone, 6D Skull tracking with Iris, and Xsight Spine tracking with Iris were −4.6%, −22.2%, and −17.8% for treatment time reduction, −1.1%, −22.0%, and −28.4% for beam reduction and −3.2%, −21.8%, and −28.1% for MU reduction, respectively. The VOLO optimizer maintains or improves the plan quality while decreases the plan complexity and improves treatment efficiency. We anticipate an increase in patient throughput with the introduction of the VOLO optimizer.

The cost of insulin has increased exponentially since its discovery in the 1920s, but the degree to which this financial burden has been transferred to patients is unknown. The present study is a retrospective analysis using claims data for privately insured patients with type 1 diabetes from 2005 to 2017. We quantify the mean annual out-of-pocket costs for insulin and diabetes-related supplies during the study period. It is imperative for health care professionals to be aware of this cost, and we hope that these findings serve as a call for legislation to cap the rising price of insulin.

Background Sexual and reproductive health (SRH) guidelines exist for adults with rheumatic diseases (RDs), but not for their adolescent counterparts. SRH discussions are regarded as important areas of discussion by pediatric rheumatology providers. However, adolescents with RDs have unique needs that are not well elucidated. The purpose of this study was to explore SRH knowledge and information sources among female adolescents with systemic lupus erythematosus (SLE), undifferentiated connective tissue disease (UCTD), mixed connective tissue disease (MCTD), or an overlap disease given their increased risk for SRH-related adverse outcomes. Methods This convergent mixed methods study sought to survey and interview participants from a healthcare system in central Pennsylvania. Eligibility included: (1) being 15–21 years old, (2) having SLE, UCTD, MCTD, or an overlap disease, (3) receiving care from a pediatric rheumatologist, (4) being assigned female at birth, and (5) having access to a computer or phone. Quantitatively, descriptive statistics and the Mann-Whitney U test were used for data presentation and assessment. Qualitatively, a semi-structured interview guide was developed, and thematic analysis was conducted. Quantitative and qualitative findings were assessed for convergence and presented using a joint display. Results Sixteen participants completed the survey, and ten also completed an individual interview. Limited understanding of SRH concepts, the impact of pregnancy, and medication safety were noted in both the survey and interview results. Participants with teratogenic medication use had lower general (2.4 vs. 2.7) and lupus-specific (3.3 vs. 4.4) SRH knowledge scores compared to their counterparts with no teratogenic medication use. Qualitatively, participants voiced concern about the interplay between their disease and pregnancy, reported differences in their information sources, and discussed trustworthy sources of SRH information. Conclusions This study identified gaps in SRH knowledge among female adolescents with susceptible RDs. Many participants are receiving SRH information from non-health care sources, but desire to have discussions on SRH with their provider. These findings signal a need for additional research in this area with expansion to other RDs and males. Clinicians and researchers can work alongside this population to develop tools and inform SRH guidelines to address patient concerns and improve overall outcomes.

To compare the proportion of female and male fetuses classified as microcephalic (head circumference [HC] < 3rd percentile) and macrocephalic (>97th percentile) by commonly used sex-neutral growth curves. For fetuses evaluated at a single center, we retrospectively determined the percentile of the first fetal HC measurement between 16 and 0/7 and 21-6/7 weeks using the Hadlock, Intergrowth-21st, and NICHD growth curves. The association between sex and the likelihood of being classified as microcephalic or macrocephalic was evaluated with logistic regression. Female fetuses (  = 3,006) were more likely than male fetuses (  = 3,186) to be classified as microcephalic using the Hadlock (0.4% male, 1.4% female; odds ratio female vs. male 3.7, 95% CI [1.9, 7.0],  < 0.001), Intergrowth-21st (0.5% male, 1.6% female; odds ratio female vs. male 3.4, 95% CI [1.9, 6.1],  < 0.001), and NICHD (0.3% male, 1.6% female; odds ratio female vs. male 5.6, 95% CI [2.7, 11.5],  < 0.001) curves. Male fetuses were more likely than female fetuses to be classified as macrocephalic using the Intergrowth-21st (6.0% male, 1.5% female; odds ratio male vs. female 4.3, 95% CI [3.1, 6.0],  < 0.001) and NICHD (4.7% male, 1.0% female; odds ratio male vs. female 5.1, 95% CI [3.4, 7.6],  < 0.001) curves. Very low proportions of fetuses were classified as macrocephalic using the Hadlock curves (0.2% male, < 0.1% female; odds ratio male vs. female 6.6, 95% CI [0.8, 52.6]). Female fetuses were more likely to be classified as microcephalic, and male fetuses were more likely to be classified as macrocephalic. Sex-specific fetal head circumference growth curves could improve interpretation of fetal head circumference measurements, potentially decreasing over- and under-diagnosis of microcephaly and macrocephaly based on sex, therefore improving guidance for clinical decisions. Additionally, the overall prevalence of atypical head size varied using three growth curves, with the NICHD and Intergrowth-21st curves fitting our population better than the Hadlock curves. The choice of fetal head circumference growth curves may substantially impact clinical care.

Breast arterial calcification (BAC), detected on routine mammography, is the calcification of medial arteries. BAC has been suggested to be linked to cardiovascular disease (CVD) risk. A systematic search was done that identified studies examining BAC, CVD risk factors (diabetes, hypertension, dyslipidemia, smoking, obesity), cardiovascular outcomes [stroke, myocardial infarction (MI), heart failure (HF), cardiac mortality], and all-cause mortality. Additionally, an atherosclerotic CVD (ASCVD) composite outcomes including MI, stroke, and cardiac mortality was analyzed. A random-effects model was used to calculate risk ratios (RR) and odds ratio (OR) with 95% confidence intervals (CI). Heterogeneity was assessed with Q values and I2 statistics. 45 studies were included in the final meta-analysis, representing 68,584 women. BAC prevalence was 17.1%. Among cross-sectional studies, BAC was associated with diabetes (OR: 1.97, 95% CI: 1.71–2.27, I2= 70.78%), hypertension (OR: 1.82, 95% CI: 1.52–2.18, I2 = 88.3%), and hyperlipidemia (OR: 1.24, 95% CI: 1.06–1.45, I2 = 76.4%). BAC was negatively associated with smoking (OR: 0.50, 95% CI: 0.41–0.61, I2 = 78.4%). BAC was associated with known CVD (OR: 2.71, 95% CI: 2.13–3.45, I2 = 76.7%). Among cohort studies, BAC was associated with incident stroke (RR: 2.05, 95% CI: 1.58–2.65, I2 = 50.8%), HF (RR: 2.14, 95% CI: 1.38–3.32, I2 = 87.1%), cardiac death (RR: 2.94, 95% CI: 1.32–6.54, I2 = 72.7%), ASCVD (RR: 1.58, 95% CI: 1.23–2.04 I2 = 81.9%) and all-cause mortality (RR: 2.04, 95% CI: 1.08–3.84, I2 = 96.78%). Significant interstudy heterogeneity in this meta-analysis is a limitation on confidence in the pooled results. In conclusion, BAC observed on mammography may serve as a marker for increased CVD risk and mortality in women; however, future research is needed to standardize BAC assessment and confirm its clinical utility in CVD risk stratification.

BackgroundThe HERO registry was established to support research on the impact of the COVID-19 pandemic on US healthcare workers.ObjectiveDescribe the COVID-19 pandemic experiences of and effects on individuals participating in the HERO registry.DesignCross-sectional, self-administered registry enrollment survey conducted from April 10 to July 31, 2020.SettingParticipants worked in hospitals (74.4%), outpatient clinics (7.4%), and other settings (18.2%) located throughout the nation.ParticipantsA total of 14,600 healthcare workers.Main MeasuresCOVID-19 exposure, viral and antibody testing, diagnosis of COVID-19, job burnout, and physical and emotional distress.Key ResultsMean age was 42.0 years, 76.4% were female, 78.9% were White, 33.2% were nurses, 18.4% were physicians, and 30.3% worked in settings at high risk for COVID-19 exposure (e.g., ICUs, EDs, COVID-19 units). Overall, 43.7% reported a COVID-19 exposure and 91.3% were exposed at work. Just 3.8% in both high- and low-risk settings experienced COVID-19 illness. In regression analyses controlling for demographics, professional role, and work setting, the risk of COVID-19 illness was higher for Black/African-Americans (aOR 2.32, 99% CI 1.45, 3.70, p < 0.01) and Hispanic/Latinos (aOR 2.19, 99% CI 1.55, 3.08, p < 0.01) compared with Whites. Overall, 41% responded that they were experiencing job burnout. Responding about the day before they completed the survey, 53% of participants reported feeling tired a lot of the day, 51% stress, 41% trouble sleeping, 38% worry, 21% sadness, 19% physical pain, and 15% anger. On average, healthcare workers reported experiencing 2.4 of these 7 distress feelings a lot of the day.ConclusionsHealthcare workers are at high risk for COVID-19 exposure, but rates of COVID-19 illness were low. The greater risk of COVID-19 infection among race/ethnicity minorities reported in the general population is also seen in healthcare workers. The HERO registry will continue to monitor changes in healthcare worker well-being during the pandemic.Trial RegistrationClinicalTrials.gov identifier NCT04342806

Pediatric Long COVID has been associated with a wide variety of symptoms, conditions, and organ systems, but distinct clinical presentations, or subphenotypes, are still being elucidated. In this exploratory analysis, we identified a cohort of pediatric (age <21) patients with evidence of Long COVID and no pre-existing complex chronic conditions using electronic health record data from 38 institutions and used an unsupervised machine learning-based approach to identify subphenotypes. Our method, an extension of the Phe2Vec algorithm, uses tens of thousands of clinical concepts from multiple domains to represent patients’ clinical histories to then identify groups of patients with similar presentations. The results indicate that cardiorespiratory presentations are most common (present in 54% of patients) followed by subphenotypes marked (in decreasing order of frequency) by musculoskeletal pain, neuropsychiatric conditions, gastrointestinal symptoms, headache, and fatigue.