我国日间手术开展现状与前景展望

日间手术在国外已有上百年的发展历史，现已成为欧美国家重要手术模式。我国于20世纪初开始开展日间手术，但目前尚未普及，发展不平衡问题比较突出，存在认识不清、开展不规范、与医保支付对接不畅等问题。日间手术是一种使国家、医院和病人三方均受益的新型手术模式。近年来，国家相关管理部门积极引导，开展日间手术的医院明显增多，可以预见，我国日间手术即将进入快速发展的新阶段。因此，有条件的医院可以从简单、易操作的病种开始，落实临床路径，积累经验，再逐步稳妥展开。在保证质量的前提下，不断拓展日间手术范围，提高三、四级手术比例。同时，积极与医保支付政策对接，采取灵活的方式，获得医保的支持，更好地促进我国日间手术的快速发展。     

Measuring the appropriateness of carbamazepine and valproic acid prescribing and utilization using a newly implemented online system in the Tabuk Region of Saudi Arabia

Epilepsy is a common neurologic disorder, which is efficiently treated with carbamazepine and valproic acid. Moreover, Saudi Ministry of Health implemented a new E-system for Poison Control Centers called Awtar to enhance technology utilization in ensuring patients’ satisfaction and to improve treatment outcomes. Therefore, we conducted this study to assess appropriateness of indication of requests and therapeutic levels of carbamazepine and valproic acid in Tabuk area, North West Saudi Arabia. This is a retrospective observational study conducted in Poison Control & Forensic Chemistry Center, Tabuk, Saudi Arabia. Patients’ data were obtained for years 2018 and 2019. The blood levels of carbamazepine and valproic acid were measured by Therapeutic Drug Monitoring (TDM) Unit. We selected patients treated with either valproic acid or carbamazepine alone without any history of drug allergy. Data of 264 patients were extracted from Awtar E-system. Serum carbamazepine levels were within therapeutic range in 114 patients (75.50%), above-therapeutic range in 13 patients (8.61%) and sub-therapeutic levels in 24 patients (15.89%). Regarding serum valproic acid, it is within therapeutic range in 62 patients (54.87%), above-therapeutic range in 11 patients (9.73%) and sub-therapeutic levels in 40 patients (35.40%). In conclusion, this study gives information about partial appropriateness of usage of carbamazepine and low level of appropriateness of valproic acid. However, more efforts are needed to improve results of appropriateness of indication of antiepileptic drugs.     

贵州省"5+3"模式订单定向医学毕业生的离职意愿及影响因素研究

背景 我国基层全科医生的离职意愿较高，调查其离职意愿并分析影响因素，可以为减少基层卫生人才流失提供思路。目前，完成"5+3"模式（5年临床医学本科教育+3年住院医师规范化培训）培养的订单定向医学毕业生逐步履约进入基层工作，而针对该部分全科医生离职意向的研究相对较少。 目的 调查贵州省"5+3"模式订单定向医学毕业生回归基层工作后的离职意愿及影响因素，为完善吸引卫生人才留任、建设基层全科医生队伍相关政策提供依据。 方法 以贵州省截至2020年底已完成"5+3"模式培养并履约到基层医疗卫生机构工作的2015—2017级订单定向医学毕业生为研究对象。于2021-01-20至2021-02-10对其开展电子问卷调查，内容包括毕业生的一般情况、职业满意度、离职意愿、服务期满后职业方向。共回收问卷347份，其中有效问卷311份，问卷有效回收率为89.6%。采用单因素分析及多元逐步线性回归分析全科医生离职意愿的影响因素。 结果 贵州省"5+3"订单定向医学毕业生的整体离职意愿得分为（3.98±0.98）分，具有离职倾向者229例（73.6%）。不同性别、单位地理位置、每日工作量者的离职意愿得分比较，差异有统计学意义（P<0.05）。多元逐步线性回归分析显示，单位负责人对待下属的方式、在工作中获得的成就感、对当前收入满意程度、家人对工作的支持程度、当地激励政策执行程度是"5+3"订单定向医学毕业生离职意愿的影响因素（P<0.05）。服务期满后，计划留任原基层医疗卫生机构者12例（3.9%），计划去其他基层医疗卫生机构者21例（6.7%），计划离开基层去上级医院工作者196例（63.0%），计划攻读全日制硕士学位者60例（19.3%）。 结论 贵州省"5+3"模式订单定向医学毕业生的离职意愿较高，预计服务期满后基层全科人才流失较多，需从提高收入、重视全科医生心理需求、优化全科医生培养与使用、发展基层医疗卫生机构、加强全科宣传等方面着手改善。     

Sliding mode control of time‐varying delay Markov jump with quantized output

This paper investigates the quantized sliding mode control of Markov jump systems with time‐varying delay. A dynamical adjustment law is explored to quantize the system output. By constructing an observer‐based integral sliding surface, a sliding mode controller is designed to take over the dynamical motion of state estimation and ensure the reachability of sliding surface. A new scaling manner is developed to build the bound between the system output and quantized error. With the help of separation strategies for controller synthesis and general transition probabilities and a lower bound theorem for nonlinear integral terms, a new synthesis method to ensure the required stability and meet the required performance is proposed in the form of linear matrix inequalities. The validity of the proposed control method is illustrated by a numerical example.     

Real-World Palbociclib Use in HR+/HER2− Advanced Breast Cancer in Canada: The IRIS Study

Background: Palbociclib is a selective cyclin-dependent kinase (CDK) 4/6 inhibitor used in combination with aromatase inhibitors or fulvestrant for patients with hormone receptor-positive (HR+) human epidermal growth factor receptor 2 (HER2)-negative advanced/metastatic breast cancer (ABC/MBC). Palbociclib was the first CDK 4/6 inhibitor approved for HR+/HER2− ABC/MBC treatment in Canada in combination with letrozole (P+L) as an initial endocrine-based therapy (approved March 2016), or with fulvestrant (P+F) following disease progression after prior endocrine therapy (approved May 2017). The Ibrance Real World Insights (IRIS) study ({"type":"clinical-trial","attrs":{"text":"NCT03159195","term_id":"NCT03159195"}}NCT03159195) collected real-world outcomes data for palbociclib-treated patients in several countries, including Canada. Methods: This retrospective chart review included women with HR+/HER2− ABC/MBC receiving P+L or P+F in Canada. Physicians reviewed medical records for up to 14 patients, abstracting demographic and clinical characteristics, treatment patterns, and clinical outcomes. Progression-free rates (PFRs) and survival rates (SRs) at 6, 12, 18, and 24 months were estimated via Kaplan–Meier analysis. Results: Thirty-three physicians examined medical records for 247 patients (P+L, n = 214; P+F, n = 33). Median follow-up was 8.8 months for P+L and 7.0 months for P+F. Most patients were initiated on palbociclib 125 mg/d (P+L, 90.2%; P+F, 84.8%). Doses were reduced in 16.6% of P+L and 14.3% of P+F patients initiating palbociclib at 125 mg/d. The PFR for P+L was 90.3% at 12 months and 78.2% at 18 months; corresponding SRs were 95.6% and 93.0%. For P+F, 6-month PFR was 91.0%; 12-month SR was 100.0%. Conclusions: Dose reduction rates were low and PFR and SR were high in this Canadian real-world assessment of P+L and P+F treatments, suggesting that palbociclib combinations are well tolerated and effective.     

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

BackgroundUnequal housing access resulted in more than 150 million homeless people worldwide, with millions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research.ResultsOur findings indicated a geographic clustering of case study locations– 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters.ConclusionMost studies were restricted in analyzing and visualizing the patterns and disease clusters; however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.     

From the "what" to the "how": Teaching integrative medicine-related skills to medical students during COVID-19

ObjectiveTo examine the impact of an integrative medicine (IM) course on self-perceived IM-related communication and research skills.MethodsA 3-day mandatory "hybrid" (online and in-person) IM course was held within COVID-19 restrictions for 161 pre-clerkship medical students, with workshops facilitated by mentor healthcare professionals (IM and non-IM) and student-directed tasks. Self-perceived levels of 6 IM-related skills were scored (from 1 to 5) for history-taking; communicating with patients with "alternative" health-beliefs; referral to IM consultations; assessing risks/benefits; and working with non-medical IM practitioners.Results137 students (85.1%) completed pre-/post-course questionnaires, with overall scores improving from pre-course (1.98 ± 0.92) to post-course (3.31 ± 0.63; p < 0.0001), for the entire group and student subgroups (with vs. without prior IM experience). Multivariate analysis found no association between age, gender, primary language or prior experience with IM and improvement in skill scores.ConclusionsThe IM course increased self-perceived skill levels, reflecting the course curriculum and workshops. Further research needs to explore the application of these skills during clinical training.Practice implicationsTeaching medical students about IM in a course comprising communication and research skills was shown to be feasible and effective. The application of IM-related skills needs to be evaluated during the clinical clerkship.     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).