Displaying all 2 publications

Abstract:
Sort:
  1. Hussain S, Gul M, Dhar S
    Malays Orthop J, 2014 Mar;8(1):8-13.
    PMID: 25279078 DOI: 10.5704/MOJ.1403.011
    BACKGROUND: Proximal humeral fractures are considered the last unsolved fractures in orthopaedics. The treatment is controversial and various operative modalities have been reported in the literature. The aim of the present study was to evaluate functional outcome and complication rate after open reduction and internal fixation of displaced proximal humerus fractures by proximal humerus AO stainless steel T-plate. Twenty-five (25) patients with displaced proximal humerus fractures treated with proximal humerus T-plate between May 2005 and June 2008 were included in the study. Fractures were classified according to the Neer classification into displaced 2-part, 3-part, and 4-part fractures. Patients were followed-up for a minimum period of two years. Functional evaluation was done according to the Neer scoring system. Scores were compared with other studies in the literature using similar implant. Twenty patients had 2-part fracture, four had 3-part fracture, and one had 4-part fracture. Eighty-eight [88% (n = 22)] patients had good to excellent result, eight [8% (n = 2)] had fair, and four [4% (n = 1)] had poor result. Difference in Neer's score between 2-part and 3-part fractures was not significant. Complications encountered in this series were screw backout in 8% (n = 2), superficial infection in 12% (n = 3), and avascular necrosis in 4% (n = 1) of cases. We conclude that proximal humerus AO T- plate is a cheap and easily available implant, aspects which are particularly relevant in third world countries like India. It gives reliable fixation for 2-part and 3-part fractures. Its use in more complicated fracture patterns of 4-part fractures is not recommended.

    KEY WORDS: Proximal humerus fractures, proximal humerus stainless steel T-plate, unstable fracture.

  2. Khan A, Gul MA, Zareei M, Biswal RR, Zeb A, Naeem M, et al.
    Comput Intell Neurosci, 2020;2020:7526580.
    PMID: 32565772 DOI: 10.1155/2020/7526580
    With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links