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Kaufman Khan posted an update 6 months ago
To effect a patient-centric model, relying on interprofessional cooperation and the strategic utilization of healthcare providers’ practice domains. This research project will aim to (1) assess the model’s effect on FMG operational performance and its users’ resource consumption within the public health system, (2) explore its enhancement regarding professional roles, interprofessional collaboration, and patient-centeredness, and (3) record users’ experiences with the model. The research protocol, described in this article, will be used for this study.
For the implementation, a type 2 model will be integrated into a hybrid approach. We will be collecting data that encompasses both quantitative and qualitative measurements. The quantitative analysis, necessitated by the single-unit nature of this intervention study, will utilize either synthetic control methods and/or one-sample generalized linear models for analyses at the FMG level. To appraise the broad ramifications stemming from
Mixed-effects models, alongside propensity score matching, will form the backbone of our analysis pertaining to the public health system. In our qualitative research, an interpretative and descriptive approach will be employed to narrate user experiences and identify the variables contributing to an improved scope of professional practice, collaborative procedures, and patient-centric care. Healthcare providers, administrative staff, and stakeholders will each be subject to individual, in-depth, semi-structured interviews.
The model’s implementation, alongside the significance for patient experiences.
The Sectoral Research in Population Health and Primary Care Ethics Committee of the Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale (#2019-1503) deemed this study to be ethically sound and approved it. At various scientific gatherings, along with the stakeholders involved in the advisory committees, the results of the investigation will be presented. Manuscripts will undergo the peer-review process before journal publication.
This study received ethical clearance from the Ethics Committee of the Sectoral Research in Population Health and Primary Care of the Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale, specifically project #2019-1503. The stakeholders involved in the advisory committees, and attendees at several scientific conferences, will receive the investigation’s findings. Manuscripts will be sent to peer-reviewed journals in the publishing process.
Quantifying the impact of interprofessional primary care on patient well-being and health system effectiveness is important to gauge its success and to shape quality improvement strategies. This scoping review maps the literature on primary care performance measurement indicators to evaluate how effectively current indicators represent, or could be adapted to represent, the processes, outputs, and outcomes of interprofessional primary care.
The six-stage framework, as articulated by Arksey and O’Malley (2005), will direct the review. Studies focused on performance indicators, frameworks, interprofessional teams, and primary care, published in English or French between 2000 and 2022, will be located via a comprehensive search including MEDLINE, Embase, CINAHL, grey literature, and the reference lists of core studies. Two reviewers will independently assess all abstracts and full-text studies for suitability for inclusion. Indicators deemed eligible will be categorized based on the process, output, and outcome domains specified in two validated frameworks. Commencing in November 2022, the study’s projected end date is July 2023.
This review undertaking does not entail an ethical review requirement. A peer-reviewed publication, presentations to stakeholders, and presentations at conferences will collectively be employed to disseminate the results.
No ethical clearance is needed for this review. Dissemination of the results will occur through peer-reviewed publications, presentations at conferences, and presentations to stakeholders.
Deep brain stimulation (DBS) implantation, carried out under general anesthesia (GA), has been a therapeutic approach for Parkinson’s disease (PD) patients experiencing significant comorbidities or disabling off-medication symptoms. Nonetheless, general anesthetic administration can potentially affect intraoperative microelectrode recording (MER) measurements in a way that is not uniform. The existing body of research regarding the influence of sedatives and general anesthetics on multi-unit activity recorded by MER in Parkinson’s Disease patients during Deep Brain Stimulation procedures is quite meager. Consequently, the impact of anesthetic selection on MER continues to be indeterminate.
At Beijing Tiantan Hospital, Capital Medical University, a prospective, randomized, controlled, non-inferiority study will take place. Subthalamic nucleus (STN)-deep brain stimulation (DBS) patients, scheduled for bilateral procedures, will only be enrolled after a rigorous eligibility review. One hundred and eighty-eight patients will be randomized to either conscious sedation (CS) or general anesthesia (GA) in an 11:1 ratio. The outcome of primary interest is the percentage of high normalised root mean square (NRMS) values observed in the MER signal.
The Ethics Committee of Beijing Tiantan Hospital of Capital Medical University (KY2022-147-02) granted approval for the study. Should studies on GA using desflurane yield negative results, this will suggest that the anesthetic’s effect on MER during STN-DBS is on par with the effect of CS. This clinical trial’s results will be disseminated through presentations at national and international conferences, and publication in a peer-reviewed journal.
The study identified by NCT05550714.
Information concerning the clinical trial NCT05550714.
Worldwide, diabetes has become more prevalent, resulting in a substantial health and economic burden. Controlling its prevalence hinges on the capacity for early prediction.
A cohort study, with a prospective design.
National research involving Irish representatives.
The study sample comprised 8504 participants aged 50 years or more.
To gauge various aspects of social, financial, health, mental, and familial statuses, surveys amassed data exceeding 40,000 variables. Feature selection procedure involved logistic regression. A variety of machine learning algorithms were trained, encompassing distributed random forests, extremely randomized trees, a generalized linear model with regularization, a gradient boosting machine, and a deep neural network. An optimal model was created by integrating these algorithms into a layered ensemble. Different performance metrics were employed for evaluating the model, specifically AUC, log loss, mean per-classification error, MSE, and RMSE. To understand the established model, the SHapley Additive exPlanations (SHAP) approach was utilized.
Analysis spanning two years pinpointed 105 baseline characteristics, including sex, low-density lipoprotein cholesterol levels, and cirrhosis, as key contributors to the likelihood of developing diabetes. Predicting diabetes risk, the superior model exhibited high accuracy, robustness, and discrimination, as evidenced by an AUC of 0.854, a log loss of 0.187, a mean per classification error of 0.0267, an RMSE of 0.0229, and an MSE of 0.0052 in the independent test set. The model’s calibration was also observed to be precise. Insight into the decision-making process of the model was provided by the SHAP algorithm.
Early recognition of high-risk patients is possible through these findings, allowing physicians to implement targeted interventions in an effort to reduce diabetes occurrence.
To reduce diabetes incidence, these findings empower physicians to identify high-risk patients early and execute targeted interventions.
The quickening pace of changes in cancer treatment modalities makes finding the optimal treatment sequence difficult. Evaluations of treatments in clinical decision-making are often confined to single-point studies, thereby restricting the ability to identify delayed impacts of past treatments on the success and suitability of future treatment strategies. Dynamic treatment regimes (DTRs) evaluations are attracting increasing attention due to their capacity for providing individualized treatment plans based on the evolving patient and treatment characteristics. This scoping review systematically maps the evaluation of DTRs across oncology clinical trials to build evidence for clinical decision-making.
Using the concept of DTR, we will meticulously search MEDLINE (PubMed), Web of Science, Scopus, and the WHO International Clinical Trials Registry Platform to identify clinical trials focused on treatment sequencing in oncology, encompassing both experimental and observational studies, and encompassing protocols of ongoing trials. Information regarding cancer cases, clinical settings, treatments, customized parameters, decision strategies, decision points, and results will be included in the data extraction process. This will also entail the type of data, the study design, and the statistical procedures employed for DTR assessment. mk-4827 inhibitor The Joanna Briggs Institute Reviewer’s manual for scoping reviews will guide the review process. Patient involvement is explicitly excluded.
The secondary analysis of published literature inherent in this scoping review exempts it from requiring ethics committee approval. Through presentations at relevant conferences and peer-reviewed scientific journals, the findings will be disseminated. This scoping review will provide a clearer picture of the methodologies used to generate evidence on treatment sequencing in oncology, assisting in identifying knowledge and methodological deficiencies that necessitate further investigation.
Given that this scoping review will analyze previously published materials, no ethics committee approval is needed. The results will be communicated through peer-reviewed scientific publications and presentations at pertinent conferences.
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