Evaluation Of Diploma In Family Medicine Ensuring Quality Through CIPP Model

Background: To ensure quality of Diploma in Family medicine (DFM). An evaluation was conducted to determine the components and outcomes of the course for further development of the program. Methods: A Cross-sectional study was conducted with 15 students of three batches of DFM. Data collection form was designed on CIPP evaluation approach. It was used to collect information regarding goals and objectives, implementation strategy, satisfaction of instructor and trainees and impact of the program on the target population Descriptive measures (frequency and percentage distributions) were used to analyze the data on SPSS 19. Results: Majority (92%) were satisfied with the course content, organization, learning environment and teaching methods of Family medicine rotation. Most of the respondents (92%) were also satisfied with learning recourses accessible and assessment methodologies employed for ongoing assessment. They found ‘the atmosphere conducive for learning’ ,‘the rotations were a good experience, well organized with learning objectives provided at all rotations’ some suggestions made by students for improvement of the course ‘were teaching should be more interactive and more opportunity should be provided to participate with consultants’ The top rated rotations were dermatology, emergency medicine, ENT and orthopedics; where organizational learning environment and quality of delivery were considered as the best parts. Conclusion: The program was found successful in achieving its broad objectives. The graduating students found the training effective in enhancing team building abilities, independent thinking, analytical and problem solving skills and professional development. They were very satisfied with the administrative support (67%), infrastructure of the department (33%) and guidance by supervisors (67%). Key Words: Evaluation, Family medicine, Diplom

Undertaking multi-centre randomised controlled trials in primary care: learnings and recommendations from the PULsE-AI trial researchers

Background Conducting effective and translational research can be challenging and few trials undertake formal reflection exercises and disseminate learnings from them. Following completion of our multicentre randomised controlled trial, which was impacted by the COVID-19 pandemic, we sought to reflect on our experiences and share our thoughts on challenges, lessons learned, and recommendations for researchers undertaking or considering research in primary care. Methods Researchers involved in the Prediction of Undiagnosed atriaL fibrillation using a machinE learning AlgorIthm (PULsE-AI) trial, conducted in England from June 2019 to February 2021 were invited to participate in a qualitative reflection exercise. Members of the Trial Steering Committee (TSC) were invited to attend a semi-structured focus group session, Principal Investigators and their research teams at practices involved in the trial were invited to participate in a semi-structured interview. Following transcription, reflexive thematic analysis was undertaken based on pre-specified themes of recruitment, challenges, lessons learned, and recommendations that formed the structure of the focus group/interview sessions, whilst also allowing the exploration of new themes that emerged from the data. Results Eight of 14 members of the TSC, and one of six practices involved in the trial participated in the reflection exercise. Recruitment was highlighted as a major challenge encountered by trial researchers, even prior to disruption due to the COVID-19 pandemic. Researchers also commented on themes such as the need to consider incentivisation, and challenges associated with using technology in trials, especially in older age groups. Conclusions Undertaking a formal reflection exercise following the completion of the PULsE-AI trial enabled us to review experiences encountered whilst undertaking a prospective randomised trial in primary care. In sharing our learnings, we hope to support other clinicians undertaking research in primary care to ensure that future trials are of optimal value for furthering knowledge, streamlining pathways, and benefitting patients

Knowledge, attitudes, and practices of the general population of Pakistan regarding typhoid conjugate vaccine: findings of a cross-sectional study

Typhoid fever, a common enteric disease in Pakistan, caused by Salmonella typhi, is becoming an extended drug-resistant organism and is preventable through the typhoid conjugate vaccine (TCV). Public adherence to preventive measures is influenced by knowledge and attitude toward the vaccine. This study investigates the knowledge, attitudes, and practices of the general population of Pakistan toward TCV. The differences in mean scores and factors associated with typhoid conjugate vaccine knowledge, attitudes, and practices were investigated. A total of 918 responses were received with a mean age of 25.9 ± 9.6, 51% were women, and 59.6% had graduation-level education. The majority of them responded that vaccines prevent illness (85.3%) and decrease mortality and disability (92.6%), and typhoid could be prevented by vaccination (86.7%). In total, 77.7 and 80.8% considered TCV safe and effective, respectively. Of 389 participants with children, 53.47% had vaccinated children, according to the extended program on immunization (EPI). Higher family income has a higher odds ratio (OR) for willingness toward booster dose of TCV [crude odds ratio (COR) = 4.920, p–value <0.01; adjusted odds ratio (aOR) = 2.853, value of p <0.001], and negative attitude regarding the protective effect of TCV has less willingness toward the booster dose with statistical significance (COR = 0.388, value of p = 0.017; aOR = 0.198, value of p = 0.011). The general population of Pakistan had a good level of knowledge about the benefits of TCV, and attitude and practices are in favor of the usage of TCV. However, a few religious misconceptions are prevalent in public requiring the efforts to overcome them to promote the usage of vaccines to prevent the disease and antibiotic resistance

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial

Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged =30?years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score?=?7.4%) will be invited for a 12-lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639

Undertaking multi-centre randomised controlled trials in primary care: learnings and recommendations from the PULsE-AI trial researchers

Background: Conducting effective and translational research can be challenging and few trials undertake formal reflection exercises and disseminate learnings from them. Following completion of our multicentre randomised controlled trial, which was impacted by the COVID-19 pandemic, we sought to reflect on our experiences and share our thoughts on challenges, lessons learned, and recommendations for researchers undertaking or considering research in primary care. Methods: Researchers involved in the Prediction of Undiagnosed atriaL fibrillation using a machinE learning AlgorIthm (PULsE-AI) trial, conducted in England from June 2019 to February 2021 were invited to participate in a qualitative reflection exercise. Members of the Trial Steering Committee (TSC) were invited to attend a semi-structured focus group session, Principal Investigators and their research teams at practices involved in the trial were invited to participate in a semi-structured interview. Following transcription, reflexive thematic analysis was undertaken based on pre-specified themes of recruitment, challenges, lessons learned, and recommendations that formed the structure of the focus group/interview sessions, whilst also allowing the exploration of new themes that emerged from the data. Results: Eight of 14 members of the TSC, and one of six practices involved in the trial participated in the reflection exercise. Recruitment was highlighted as a major challenge encountered by trial researchers, even prior to disruption due to the COVID-19 pandemic. Researchers also commented on themes such as the need to consider incentivisation, and challenges associated with using technology in trials, especially in older age groups. Conclusions: Undertaking a formal reflection exercise following the completion of the PULsE-AI trial enabled us to review experiences encountered whilst undertaking a prospective randomised trial in primary care. In sharing our learnings, we hope to support other clinicians undertaking research in primary care to ensure that future trials are of optimal value for furthering knowledge, streamlining pathways, and benefitting patients

A Literature Review on Automatic Detection of Fake Profile

In the present generation, the social life of everyone has become associated with the online social networks. These sites have made a drastic change in the way we pursue our social life. Making friends and keeping in contact with them and their updates has become easier. But with their rapid growth, many problems like fake profiles, online impersonation have also grown. There is no feasible solution exist to control these problems. In this project, we came up with a framework with which automatic detection of fake profiles is possible and is efficient. This framework uses classification techniques like Support Vector Machine, Nave Bayes and Decision trees to classify the profiles into fake or genuine classes. As, this is an automatic detection method, it can be applied easily by online social networks which has millions of profile whose profiles cannot be examined manually

Automatic Detection of Fake Profiles in Online Social Network using Soft Computing

The proliferation of social media platforms and online communities has led to an increase in the creation and utilization of fake profiles for various deceptive purposes. Detecting these fake profiles is crucial to maintaining the integrity, security, and trustworthiness of online platforms. This abstract provides an overview of the techniques and challenges involved in automatically detecting fake profiles. The detection of fake profiles poses a significant challenge due to the ever-evolving strategies employed by malicious actors. However, researchers and platform developers have devised several techniques to tackle this problem. Profile completeness analysis examines the information provided by users, such as profile pictures, connections, and consistency of details. Sparse or inconsistent data may raise suspicions of a fake profile. Image analysis involves reverse image searching and analyzing metadata to identify instances of profile picture misuse or manipulation. Linguistic analysis focuses on analyzing the language used in profile descriptions, posts, and comments. Patterns such as poor grammar, spelling mistakes, or generic content may indicate automated or fraudulent account activity. Social network analysis studies the network structure and connections between accounts, identifying clusters of suspicious profiles with similar connections. Behavioral analysis techniques aim to identify abnormal or bot-like behavior exhibited by fake profiles, such as excessive friend requests, repetitive posting patterns, or spamming. Machine learning models have emerged as powerful tools for fake profile detection. These models are trained on historical data, learning patterns and features associated with fake profiles. They can then classify new profiles based on these learned characteristics. CAPTCHA or verification tests provide an additional layer of security by deterring automated bot account creation. Despite the progress made, detecting fake profiles remains a challenge. Adversarial actors continuously adapt their strategies, making it difficult to stay ahead. The privacy concerns and ethical implications surrounding the collection and analysis of user data also present challenges. Additionally, false positives and negatives are common in automated detection, requiring continuous refinement and improvement of detection techniques

Cutaneous signs of selected cardiovascular disorders: A narrative review

Cardiovascular diseases are the leading cause of mortality and morbidity globally. Clinicians must know cutaneous signs of cardiovascular disease, including petechiae, macules, purpura, lentigines, and rashes. Although cutaneous manifestations of diseases like infectious endocarditis and acute rheumatic fever are well established, there is an indispensable need to evaluate other important cardiovascular diseases accompanied by cutaneous signs. Moreover, discussing the latest management strategies in this regard is equally imperative. This review discusses distinctive skin findings that help narrow the diagnosis of cardiovascular diseases and recommendations on appropriate treatment

Using machine learning to predict anticoagulation control in atrial fibrillation: A UK Clinical Practice Research Datalink study

Objective: To investigate the predictive performance of machine learning (ML) algorithms for estimating anticoagulation control in patients with atrial fibrillation (AF) who are treated with warfarin. Methods: This was a retrospective cohort study of adult patients (≥18 years) between 2007 and 2016 using linked primary and secondary care data (Clinical Practice Research Datalink GOLD and Hospital Episode Statistics). Various ML techniques were explored to predict suboptimal anticoagulation control, defined as time in therapeutic range (TTR) 80 years and <70 kg, respectively). Addition of time-varying data to the LSTM NN improved predictive performance, plateauing at AUC of 0.830 at 30 weeks. Conclusion: ML algorithms displayed clinically useful ability to predict patients who are at greater risk of suboptimal control. The addition of time-varying data to the algorithm, especially prior INR measurements, improved predictive performance. These algorithms provide improved predictive tools for identifying patients who may benefit from more frequent INR monitoring or switching to alternative therapies

Predicting atrial fibrillation in primary care using machine learning

BACKGROUND:Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of the undiagnosed may be supported by risk-prediction models relating patient factors to AF risk. However, there exists a need for an implementable risk model that is contemporaneous and informed by routinely collected patient data, reflecting the real-world pathology of AF. METHODS:This study sought to develop and evaluate novel and conventional statistical and machine learning models for risk-predication of AF. This was a retrospective, cohort study of adults (aged ≥30 years) without a history of AF, listed on the Clinical Practice Research Datalink, from January 2006 to December 2016. Models evaluated included published risk models (Framingham, ARIC, CHARGE-AF), machine learning models, which evaluated baseline and time-updated information (neural network, LASSO, random forests, support vector machines), and Cox regression. RESULTS:Analysis of 2,994,837 individuals (3.2% AF) identified time-varying neural networks as the optimal model achieving an AUROC of 0.827 vs. 0.725, with number needed to screen of 9 vs. 13 patients at 75% sensitivity, when compared with the best existing model CHARGE-AF. The optimal model confirmed known baseline risk factors (age, previous cardiovascular disease, antihypertensive medication usage) and identified additional time-varying predictors (proximity of cardiovascular events, body mass index (both levels and changes), pulse pressure, and the frequency of blood pressure measurements). CONCLUSION:The optimal time-varying machine learning model exhibited greater predictive performance than existing AF risk models and reflected known and new patient risk factors for AF