Notes

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There is an inherent tension between standardization and customization of care delivery processes. The challenge for health care systems is to achieve the right balance. At its best, standardized work can create efficiencies that generate the additional time needed for personalized care. Similarly, at its best, customization allows the people within a system to accommodate the needs, preferences, and circumstances of the unique individuals and local communities they serve. We provide examples and offer principles to decide when standardization offers the most successful path and when customization may be preferred. We believe that, in sum, the balance has shifted too far toward standardization and that a rebalancing toward customization will benefit patients, clinicians, and the health care system.Expedited partner therapy involves prescribing sexually transmitted infection (STI) treatment for a patient's partner(s) without seeing the partner. It is approved for heterosexual partners of patients with chlamydia in most states. However, the Centers for Disease Control and Prevention recommends against expedited partner therapy in men-who-have-sex-with-men (MSM), citing limited data in this population and concerns that expedited partner therapy could discourage comprehensive STI testing, thereby driving increased HIV transmission. In this piece, we describe the case of a 33-year-old gay man on HIV pre-exposure prophylaxis (PrEP) whose cycle of chlamydia reinfection might have been prevented by expedited partner therapy. His case highlights how new HIV prevention strategies-including PrEP and Treatment as Prevention-challenge the assumption that all MSM with chlamydia are at risk for HIV. Until more data on expedited partner therapy in MSM are available, clinicians should incorporate characteristics of patients' sexual networks in weighing the risks and benefits of expedited partner therapy.
Lower urinary tract symptoms are very common in older men. HPK1-IN-2 order We conducted a systematic review and meta-analysis to evaluate the effects of self-management interventions on these symptoms.

We included randomized controlled trials comparing the effect of self-management interventions (alone or combined with drug therapy) with usual care or drug therapy alone in men with lower urinary tract symptoms. Two independent reviewers screened retrieved articles, extracted data, and assessed the risk of bias of included studies. The primary outcome was lower urinary tract symptom severity. Where data were available, we calculated mean differences (MDs) between the interventions.

Analyses were based on 8 studies among 1,006 adult men. Seven of these studies were judged to be at high risk in 2 of the 7 domains of bias. The nature of the self-management interventions varied across studies. There was a clinically important reduction in the 35-point International Prostate Symptom Score at 6 months favoring self-management interventions compared with usual care (MD = -7.4; 95% CI, -8.8 to -6.1; 2 studies). The reduction in score with self-management was similar to that achieved with drug therapy at 6 to 12 weeks (MD = 0.0; 95% CI, -2.0 to 2.0; 3 studies). Self-management had a smaller, additional benefit at 6 weeks when added to drug therapy (MD = -2.3; 95% CI, -4.1 to -0.5; 1 study).

We found moderate-quality evidence (suggesting reasonable certainty in estimates) for the effectiveness of self-management for treating lower urinary tract symptoms in men. We therefore recommend the use of self-management interventions for this patient population.
We found moderate-quality evidence (suggesting reasonable certainty in estimates) for the effectiveness of self-management for treating lower urinary tract symptoms in men. We therefore recommend the use of self-management interventions for this patient population.
We developed and implemented a new model of collaborative care that includes a triage and referral management system. We present initial implementation metrics using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework.

Primary care clinicians in 8 practices referred patients with any unmet mental health needs to the Penn Integrated Care program. Assessments were conducted using validated measures. Patients were primarily triaged to collaborative care (26%) or specialty mental health care with active referral management (70%). We conducted 50 qualitative interviews to understand the implementation process and inform program refinement. Our primary outcomes were reach and implementation metrics, including referral and encounter rates derived from the electronic health record.

In 12 months, 6,124 unique patients were referred. Assessed patients reported symptoms consistent with a range of conditions from mild to moderate depression and anxiety to serious mental illnesses inclrse group of patients with the full range of mental health conditions seen in primary care.The use of big data containing millions of primary care medical records provides an opportunity for rapid research to help inform patient care and policy decisions during the first and subsequent waves of the coronavirus disease 2019 (COVID-19) pandemic. Routinely collected primary care data have previously been used for national pandemic surveillance, quantifying associations between exposures and outcomes, identifying high risk populations, and examining the effects of interventions at scale, but there is no consensus on how to effectively conduct or report these data for COVID-19 research. A COVID-19 primary care database consortium was established in April 2020 and its researchers have ongoing COVID-19 projects in overlapping data sets with over 40 million primary care records in the United Kingdom that are variously linked to public health, secondary care, and vital status records. This consensus agreement is aimed at facilitating transparency and rigor in methodological approaches, and consistency in defining and reporting cases, exposures, confounders, stratification variables, and outcomes in relation to the pharmacoepidemiology of COVID-19. This will facilitate comparison, validation, and meta-analyses of research during and after the pandemic.
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