Background: Heel pain is a common clinical entity that requires appropriate diagnostic evaluation. Multiple clinical conditions are known to cause heel pain but plantar fasciitis is the most common. Calcaneal spurs are thought to be associated with heel pain and plantar fasciitis but conclusive studies to prove this are sparse. Plain radiography is usually the first line imaging modality for imaging patients with heel pain. Magnetic Resonance Imaging is the gold standard for diagnosing plantar fasciitis but studies have shown ultrasound to be comparable in sensitivity and specificity. The aim is to establish whether a relationship exists between calcaneal spurs and heel pain caused by plantar fasciitis.
Objective: To estimate the strength of association between plantar fasciitis and calcaneal spurs.
Methods: The study was conducted at the Aga Khan Radiology department between October 2016 and March 2017. The study design was a case-control study nested within a cross-sectional study. Patients with heel pain were recruited and had a plain radiograph and ultrasound of one or both feet. Case and control status was determined sonographically by the presence or absence of plantar fasciitis respectively.
Results: A total of 96 heels (55 participants) were evaluated and calcaneal spurs were present in 35 (36% [27%,46%]) which increased to 62 (65% [55%,74%]) on ultrasound. The crude odds ratio for the association between calcaneal spurs and plantar fasciitis was 3.8 [1.4,10.2] p=0.004. After controlling for age, sex and physical activity and accounting for within individual clustering, the adjusted odds ratio was 2.7 [0.9,8.0] p=0.072. There was a statistically significant difference in the mean calcaneal spur length measured on radiographs and ultrasound, 4.7 mm [3.7,5.7 mm] and 3.5 mm [3.0,4.0 mm], p= 0.020.
Conclusion: Calcaneal spurs and plantar fasciitis are common findings among patients with heel pain but there is no statistically significant association between them.
تهدف الدراسة الى معرفة طبيعة التنافس الدولي حول إيران بحكم موقعها الجغرافي المميز الذي يشكل حلقة وصل بين معسكرين متباينين، الدول الأوربية والولايات المتحدة الامريكية من جهة والاتحاد السوفيتي من جهة ثانية خلال الحرب العالمية الثانية عدت إيران ذات أهمية استثنائية في ظل السياسة الدولية أثناء الحرب العالمية الثانية، لا لموقعها الجغرافي المهم حسب بل انها أصبحت جزءاً لا يتجزأ من تلك السياسة، التي شكلت صراعاً مريراً وتنافساً كبيراً بين دول الحلفاء والمحور خلال تلك الحرب، وبشكل خاص بعد الهجوم الألماني على الاتحاد السوفيتي في 22 حزيران 1941 التي أخذت تطورا خطيرا في أحداث العالم بأسره. وقد اعتمد الباحث في دراسته على المنهج التاريخي الحديث حيث تابع تطور الأحداث العسكرية والعلاقات السياسية والاقتصادية بين الجانبين تاريخياً. ومن أهم النتائج التي أفرزتها الدراسة: عدم الاستقرار في العلاقات السياسية والاقتصادية بين روسيا وإيران بسبب ضغوطات الدول الأجنبية وتدخلها في الشؤون الداخلية لإيران.
Nowadays the excessive use of internet produces a huge amount of data due to the social
networks such as Twitter, Facebook, Orkut and Tumbler. These are microblogging sites
and are used to share the people opinions and suggestions on daily basis relevant to the
certain topic. These are beneficial for decision making or extracting conclusions. Analysis
of these feeds aims to assess the thinking and comments of people about some personality
or topic. Sentiment analysis is a type of text classification and is performed by various
techniques such as Machine Learning Techniques and shows that the text is negative,
positive or neutral. In this work, we provide a comparison of most recent sentiment
analysis techniques such as Na?ve Bayes, Bagging, Random Forest, Decision Tree,
Support Vector Machine and Maximum entropy. The purpose of the study is to provide an
empirical analysis of existing classification techniques for social media for analyzing the
good performance and better information retrieval. A comprehensive comparative
framework is designed to compare these techniques. Various benchmark datasets (UCI,
KAGGLE) available in different repositories are used for comparison purpose. We
presented an empirical analysis of six classifiers. The analysis results that Random Forest
performs much better as compared to other. Efforts are made to provide a conclusion
about different algorithms based on numerical and graphical metrics to conclude that
which algorithm is optimal.