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To assess the impact of resilience, the ability to withstand and bounce back from adversity, on measures of well-being, self-reported stress, and mental health diagnoses. This study was a cross-sectional survey of participants seen at an executive health practice at Mayo Clinic, Rochester, Minnesota, from January 2012 through September 2016. Participants completed an anonymous survey that included demographic information and 3 validated survey instruments-the 10-item Connor-Davidson Resilience Scale (CD-RISC), the 12-item Linear Analogue Self-Assessment Scale (LASA), and the 14-item Perceived Stress Scale (PSS). Self-reported history of mental health diagnoses was also collected. CD-RISC scores were used to stratify participants into lower (<30), medium (30-34), or higher (≥35) resilience categories. Participants' LASA scores, PSS scores, and self-reported mental health diagnoses were compared among resilience categories. Of the 2,027 eligible participants, 1,954 met the study inclusion criteria as currently employed corporate-sponsored executive or business professionals (self-designated) who completed the CD-RISC survey. Most participants (62.5%) were aged 40 to 59 years. The majority were male (78.3%), white (95.3%), educated (86.2%), and in a committed relationship (89.7%). Among participants, 41.7% reported higher resilience, 34.3% had medium resilience, and 24.0% had lower resilience. The quality of life and overall LASA scores were positively associated with higher resilience (P < .001). PSS scores and self-reported mental health diagnoses were negatively associated with higher resilience (P < .001). These associations remained significant after adjusting for patient characteristics. In this cross-sectional survey of a large cohort of corporative executives, the lower-resilience cohort had a 4-fold higher prevalence of depression and an almost 3-fold higher prevalence of anxiety compared with the higher-resilience cohort. High resilience was positively associated with well-being and negatively associated with perceived stress. Our findings suggest that higher resilience in the executive workplace environment is associated with better mental health, reduced stress, and greater well-being.

Gynecomastia, defined as benign proliferation of male breast glandular tissue, is usually caused by increased estrogen activity, decreased testosterone activity, or the use of numerous medications. Although a fairly common presentation in the primary care setting and mostly of benign etiology, it can cause patients considerable anxiety. The initial step is to rule out pseudogynecomastia by careful history taking and physical examination. A stepwise approach that includes imaging and laboratory testing to exclude neoplasms and endocrinopathies may facilitate cost-effective diagnosis. If results of all studies are normal, idiopathic gynecomastia is diagnosed. The evidence in this area is mainly of observational nature and lower quality.

Gynecomastia, defined as benign proliferation of male breast glandular tissue, is usually caused by increased estrogen activity, decreased testosterone activity, or the use of numerous medications. Although a fairly common presentation in the primary care setting and mostly of benign etiology, it can cause patients considerable anxiety. The initial step is to rule out pseudogynecomastia by careful history taking and physical examination. A stepwise approach that includes imaging and laboratory testing to exclude neoplasms and endocrinopathies may facilitate cost-effective diagnosis. If results of all studies are normal, idiopathic gynecomastia is diagnosed. The evidence in this area is mainly of observational nature and lower quality.

This commentary offers the reader an alternative to mentoring through the use of PODCASTS. By providing the listener with an understanding of the challenges and opportunities for self-reflection and sharing of experiences by the interviewees, we are impacting the listener attitudes and future goals through lessons learned.

What is the best management approach for gynecomastia? In most patients, surgical correction usually leads to immediate cosmetic and symptomatic improvement and is considered the best approach. In men who are being treated with antiandrogen therapies, pharmacological intervention with tamoxifen is the most effective approach, followed by radiotherapy. Pitfalls to avoid when treating gynecomastia Failure to detect the very rare male breast cancerOverly aggressive early intervention or evaluationAppropriate medical interventionWhen to refer to specialist treatment.

Background The benefits of a periodic health evaluation remain debatable. The incremental value added by such evaluations beyond the delivery of age appropriate screening and preventive medicine recommendations is unclear. Methods We retrospectively collected data on a cohort of consecutive patients presenting for their first episode of a comprehensive periodic health evaluation. We abstracted data on new diagnoses that were identified during this single episode of care and that were not trivial (i.e., required additional testing or intervention). Results The cohort consisted of 491 patients. The rate of new diagnoses per this single episode of care was 0.9 diagnoses per patient. The majority of these diagnoses was not prompted by patients’ complaints (71%) and would not have been identified by screening guidelines (51%). Men (odds ratio 2.67; 95% CI, 1.76, 4.03) and those with multiple complaints at presentation (odds ratio 1.12; 95% CI, 1.05, 1.19) were more likely to receive a clinically relevant diagnosis at the conclusion of the visit. Age was not a predictor of receiving a diagnosis in this cohort. Conclusion The first episode of a comprehensive periodic health evaluation may reveal numerous important diagnoses or risk factors that are not always identified through routine screening.

Background Bisphosphonates can reduce fracture risk in patients with osteoporosis, but many at-risk patients do not start or adhere to these medications. The aims of this study are to: (1) preliminarily evaluate the effect of an individualized 10-year osteoporotic fracture risk calculator and decision aid ( O STEOPOROSIS CHOICE ) for postmenopausal women at risk for osteoporotic fractures; and (2) assess the feasibility and validity (i.e., absence of contamination) of patient-level randomization (vs. cluster randomization) in pilot trials of decision aid efficacy. Methods/Design This is a protocol for a parallel, 2-arm, randomized trial to compare an intervention group receiving O STEOPOROSIS CHOICE to a control group receiving usual primary care. Postmenopausal women with bone mineral density T-scores of <-1.0, not receiving bisphosphonate therapy, and receiving care at participating primary care practices in and around Rochester, Minnesota, USA will be eligible to participate in the trial. We will measure the effect of O STEOPOROSIS CHOICE on five outcomes: (a) patient knowledge regarding osteoporosis risk factors and treatment; (b) quality of the decision-making process for both the patient and clinician; (c) patient and clinician acceptability and satisfaction with the decision aid; (d) rate of bisphosphonate use and adherence, and (e) trial processes (e.g., ability to recruit participants, collect patient outcomes). To capture these outcomes, we will use patient and clinician surveys following each visit and video recordings of the clinical encounters. These video recordings will also allow us to determine the extent to which clinicians previously exposed to the decision aid were able to recreate elements of the decision aid with control patients (i.e., contamination). Pharmacy prescription profiles and follow-up phone interviews will assess medication start and adherence at 6 months. Discussion This pilot trial will provide evidence of feasibility, validity of patient randomization, and preliminary efficacy of a novel approach -- decision aids -- to improving medication adherence for postmenopausal women at risk of osteoporotic fractures. The results will inform the design of a larger trial that could provide more precise estimates of the efficacy of the decision aid. Trial registration Clinical Trials.gov Identifier: NCT00578981

Objective To systematically review fully randomised patient preference trials and to explore the impact of preferences on attrition and outcome by meta-analysis of patient level data.Data sources Citation search using Science Citation Index and Google Scholar and search of the main electronic databases (Medline, CINAHL, Embase, and AMED) with a combination of key words.Study selection Fully randomised patient preference trials that compared treatments for any clinical condition were included. Other types of preference trials and crossover trials were excluded. Other inclusion criteria: participants aged 16 years and over; primary, self-reported outcomes measured on a continuous numerical scale. From 167 studies identified and screened, 17 were identified as fully randomised patient preference trials.Data synthesis Of the 17 trials identified, 11 authors provided raw data for the meta-analysis. Data collected were baseline and follow-up data for the main outcome, randomised allocation data, preference data, and demographic data. Baseline and first post-intervention follow-up data for the main outcome were standardised. To improve homogeneity, data for only the eight musculoskeletal trials (n=1594) were combined. To estimate the effects of preferences on outcomes and attrition, three groups were compared: patients who had a preference and were randomly allocated to their preferred treatment; patients who had a preference and were randomly allocated to the treatment they did not prefer; and patients who had no preference.Results Patients who were randomised to their preferred treatment had a standardised effect size greater than that of those who were indifferent to the treatment assignment (effect size 0.162, 95% confidence interval 0.011 to 0.314; P=0.04). Participants who received their preferred treatment also did better than participants who did not receive their preferred treatment (effect size 0.152, −0.035 to 0.339), although this was not statistically significant (P=0.11). Participants allocated to their undesired treatment had outcomes that were no different from those who were indifferent. Participants who were allocated to their undesired treatment were less likely to be lost to first follow-up compared with indifferent participants (odds ratio 1.70, 1.076 to 2.693; P=0.02). No difference was found in attrition between patients allocated to their preference and those who were indifferent.Conclusions Preferences among patients in musculoskeletal trials are associated with treatment effects. In open randomised trials, preferences should be ascertained before randomisation.

A 67-year-old woman sought medical treatment of cardiomyopathy, which had been diagnosed 2 years earlier; the causative factor was sarcoidosis. A screening mammogram revealed multiple spiculated masses in both breasts. A review of previous films obtained elsewhere showed that these masses had been increasing in prominence during the past 3 years. The patient had no visible axillary nodal abnormalities. Sarcoidosis was considered a diagnostic possibility, and a large-core needle biopsy was done with stereotactic guidance. The histological diagnosis was non-necrotizing granulomatous inflammation, consistent with sarcoidosis.