Journal of evaluation in clinical practice最新文献

Background

Social support can have health benefits but may also pose risks for the elderly, particularly those facing conflicts and network disruptions. Understanding the short and long-term mental health effects, especially in elderly individuals with chronic illnesses, is crucial due to global depression concerns. Yet, research is limited, with gaps in exploring different social disruption scenarios and lacking comprehensive multi-period data analysis.

Methods

We conducted a longitudinal study using 2016 and 2018 China Longitudinal Aging Social Survey (CLASS) data. Our study included 5476 and 7138 participants aged 60−90 with chronic illnesses, 4730 participating in both years. We analysed relationships using depression scores, social network disruption and social adaptation. Ordinary least squares explored short and long-term effects, and propensity score matching examined causal inferences, including gender and urban-rural differences. We also explored the social adaptation's mediating role.

Results

Disrupted social networks significantly increased short and long-term depression (β = 0.984, p < 0.001; β = 0.337, p < 0.01). However, after propensity score matching, only short-term impacts persisted (ATT = 0.981−1.045, p < 0.001). Factors like death and conflicts had short-term impacts, milder in females (0.673 points) than in males (1.285 points). Both urban and rural participants experienced increased depression. Social adaptation explained 14.1% of the mediation effect.

Conclusions

Disrupted social networks heightened short-term depression in elderly individuals with chronic illnesses, with varying gender and location effects. Robust social adaptability facilitated new connections, reducing depression. Implications include emphasizing rapid network recovery and enhancing adaptability for mental health support in aging populations.

Rationale

Clinical innovation—where physicians develop and use novel interventions that differ significantly from standard practice and that have not been shown to be sufficiently safe or effective for regular use in healthcare systems—has the potential to transform patient care and drive medical advancement. However, it is not without risk.

It is important, therefore, that policymakers and healthcare institutions develop strategies to encourage responsible clinical innovation. For these strategies to be effective, they need to be based on a comprehensive understanding of the factors driving physicians' development and use of innovative interventions. While research has provided important insights into contextual barriers and facilitators, individual factors, particularly physicians' interests, remain underexplored.

Aims and Objectives

The aims of this qualitative study were to investigate the factors that drive and deter clinical innovation in diverse medical specialties and to examine whether these factors differ significantly between specialties.

Method

Thirty-one semi-structured interviews were conducted with Australian physicians from surgery, reproductive medicine, and cancer care.

Results

Physicians' interests (e.g., obligations to patients and personal financial concerns) were perceived to play an important role in driving their use of innovative interventions, along with individual traits and contextual factors. There were also significant differences between specialties—with fertility specialists and surgeons more strongly emphasizing financial and commercial interests as key drivers of clinical innovation than oncologists.

Conclusions

Our findings suggest that while addressing structural barriers to clinical innovation at the health system level remains important, policymakers must also give attention to the diverse interests of physician-innovators. Understanding these interests, the ways in which they align and conflict, and which are most prominent across different specialties, will enable policymakers and healthcare institutions to develop targeted strategies to encourage physician-led innovation and ensure it is responsible.

Rationale

Systematic reviews are essential for evidence-based healthcare decision-making. While it is relatively straightforward to quantitatively assess random errors in systematic reviews, as these are typically reported in primary studies, the assessment of biases often remains narrative. Primary studies seldom provide quantitative estimates of biases and their uncertainties, resulting in systematic reviews rarely including such measurements. Additionally, evidence appraisers often face time constraints and technical challenges that prevent them from conducting quantitative bias assessments themselves. Given that multiple biases and random errors collectively skew the point estimate from the truth, it is important to incorporate comprehensive quantitative methods of uncertainty in systematic reviews. These methods should integrate random errors and biases into a unified measure of uncertainty and be easily accessible to evidence appraisers, preferably through user-friendly software.

Aims and Objectives

To address this need, we propose a posterior mixture model and introduce AppRaise, a free, web-based interactive software designed to implement this approach.

Methods

We showcase its application through a health technology assessment (HTA) report on the effectiveness of continuous glucose monitoring in reducing A1c levels among individuals with type 1 diabetes.

Results

Applying the AppRaise software to the HTA report revealed a high level of certainty (86% probability) that continuous glucose monitoring would, on average, result in a reduction in A1c levels compared with self-monitoring of blood glucose among Ontarians with type 1 diabetes. These findings were similar to other quantitative bias-adjusted approaches in systematic reviews.

Conclusion

AppRaise can be utilized as a standalone tool or as a complement to validate the quality of evidence assessed using qualitative-based scoring methods. This approach is also useful for assessing the sensitivity of parameter estimates to potential biases introduced by primary studies.

Aims and Objective

The field of medical statistics has experienced significant advancements driven by integrating innovative statistical methodologies. This study aims to conduct a comprehensive analysis to explore current trends, influential research areas, and future directions in medical statistics.

Methods

This paper maps the evolution of statistical methods used in medical research based on 4,919 relevant publications retrieved from the Web of Science. High-frequency keywords and citation metrics were analyzed to identify research hotspots. A dual-map overlay and document co-citation analysis were performed using CiteSpace to uncover thematic clusters and track knowledge flow between disciplines. Additionally, network metrics, such as betweenness centrality and sigma, were employed to quantify the influence and novelty of publications.

Results

Results identified a strong interdisciplinary exchange between medical statistics and fields such as health, nursing, molecular biology, and computer science, with clinical trials, survival analysis, and predictive modeling emerging as central themes. The influence of artificial intelligence (AI), machine learning (ML), and deep learning (DL) is growing substantially, particularly in areas such as diagnostic imaging, epidemiology, and treatment prediction, highlighting a shift towards more complex, data-driven methodologies. While traditional statistical techniques, such as survival analysis and regression, remain vital, emerging technologies are reshaping research approaches, fostering collaboration, and advancing the field's capabilities.

Conclusion

Future research will likely focus on overcoming challenges related to data privacy, ethical considerations, and the need for continued biostatistics education in healthcare. This study offers a roadmap for ongoing research and highlights opportunities for future interdisciplinary collaborations to address the complexities of modern medical data analysis. This scientometrics study reveals the evolution of statistical methods used in medical research over time, evaluates frequently cited models and thematic changes, and provides implications that can enhance evidence-based decision-making processes regarding methodological choices that guide contemporary clinical practice.

Rationale

Physicians sometimes encounter various types of gut feelings (GFs) during clinical diagnosis. The type of GF addressed in this paper refers to the intuitive sense that the generated hypothesis might be incorrect. An appropriate diagnosis cannot be obtained unless these GFs are articulated and inventive solutions are devised. Thus, the method of articulating GFs is critical.

Aims and Objectives

The current study proposes reflective verbalization (RV) to help healthcare professionals capitalize on their GF. In cognitive psychology, RV is the process of verbalizing one's thoughts and feelings through metacognition, to promote deeper understanding and insight problem-solving. When applied to clinical reasoning, RV can help doctors verbalize their GFs, refining their diagnostic hypotheses.

Method

To address GFs systematically using RV, we introduce the DATES approach, comprising five perspectives: Degree, Abandoned, Time course, Excess, and Shortage. Each perspective prompts physicians to compare their patient's information against typical illness scripts, ensuring no detail is omitted or overlooked.

Results and Conclusion

The tool also aids physicians in considering possible differential diagnoses for one or more of these elements. This guiding tool may aid physicians in overcoming biases, including confirmation and anchoring biases, thus improving diagnostic accuracy. This tool is useful for healthcare professionals who wish to improve their clinical reasoning and decision-making abilities, particularly when they encounter inexplicable contradictions in their diagnostic hypotheses.