ABSTRACT
Objectives@#Users share valuable information through online smoking cessation communities (OSCCs), which help people maintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations exist in identifying the smoking status of OSCC users (“quit” vs. “not quit”). Thus, the current study implicitly analyzed user-generated content (UGC) to identify individual users’ smoking status through advanced computational methods and real data from an OSCC. @*Methods@#Secondary data analysis was conducted using data from 3,833 users of BcomeAnEX.org. Domain experts reviewed posts and comments to determine the authors’ smoking status when they wrote them. Seven types of feature sets were extracted from UGC (textual, Doc2Vec, social influence, domain-specific, author-based, and thread-based features, as well as adjacent posts). @*Results@#Introducing novel features boosted smoking status recognition (quit vs. not quit) by 9.3% relative to the use of text-only post features. Furthermore, advanced computational methods outperformed baseline algorithms across all models and increased the smoking status prediction performance by up to 12%. @*Conclusions@#The results of this study suggest that the current research method provides a valuable platform for researchers involved in online cessation interventions and furnishes a framework for on-going machine learning applications. The results may help practitioners design a sustainable real-time intervention via personalized post recommendations in OSCCs. A major limitation is that only users’ smoking status was detected. Future research might involve programming machine learning classification methods to identify abstinence duration using larger datasets.
ABSTRACT
Objectives@#Users share valuable information through online smoking cessation communities (OSCCs), which help people maintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations exist in identifying the smoking status of OSCC users (“quit” vs. “not quit”). Thus, the current study implicitly analyzed user-generated content (UGC) to identify individual users’ smoking status through advanced computational methods and real data from an OSCC. @*Methods@#Secondary data analysis was conducted using data from 3,833 users of BcomeAnEX.org. Domain experts reviewed posts and comments to determine the authors’ smoking status when they wrote them. Seven types of feature sets were extracted from UGC (textual, Doc2Vec, social influence, domain-specific, author-based, and thread-based features, as well as adjacent posts). @*Results@#Introducing novel features boosted smoking status recognition (quit vs. not quit) by 9.3% relative to the use of text-only post features. Furthermore, advanced computational methods outperformed baseline algorithms across all models and increased the smoking status prediction performance by up to 12%. @*Conclusions@#The results of this study suggest that the current research method provides a valuable platform for researchers involved in online cessation interventions and furnishes a framework for on-going machine learning applications. The results may help practitioners design a sustainable real-time intervention via personalized post recommendations in OSCCs. A major limitation is that only users’ smoking status was detected. Future research might involve programming machine learning classification methods to identify abstinence duration using larger datasets.
ABSTRACT
Abstract Medicinal plants play a vital role in the human health care system of tribal communities and in the treatment of various gynecological problems. This study is an effort to document important medicinal flora used for the treatment of gynecological problems by indigenous people living in a tribal region near the Pak-Afghan border. The main objective of the study was to establish a clear profile of indigenous knowledge and practices from the unexplored tribal territory. Data were collected through semi-structured interviews and group discussions. The data were analyzed through Use Value and Factor of Informant Consensus. A total of 52 medicinal plants were recorded from the area; the most widely accepted were Withania somnifera (L.) Dunal (94 Use Value), Foeniculum vulgare Mill. (93 Use Value), Prunus domestica L. (91 Use Value), Myrtus communis L. (91 Use Value), Cannabis sativa L. (91 Use Value) and Nigella sativa L. (90 Use Value). A high consensus factor was recorded for menses-related problems (0.95). The root was the main part used (23% plants), followed by the leaves (20% plants), whole plant (18% plants), fruit (18% plants), and seed (13% plants). A total of 21 plants were used to treat menses-related problems, followed by sexual problems (ten plants), leucorrhea (nine plants), gastric problems (seven plants) and amenorrhea (seven plants). Knowledge related to ethnogynecological treatments is restricted to midwives and traditional healers. In conclusion, the documented flora that is particularly important to medicinal plants may be researched in the future to discover new pharmaceutical, neutraceutical and other pharmacological agents against gynecological complaints.
ABSTRACT
The case report is of a 30-year male patient who had a fracture shaft of femur and after open reduction and internal fixation developed swelling which was painful and was present on the medial aspect of the thigh. It was decided to do an angiogram as the swelling was expansile and confirmed superficial femoral artery pseudo aneurysm and the reason was thought to be over penetration of the bit of the drill while doing the surgery for shaft of femur fracture. Treatment decided by the multidisciplinary team was to remove the condylar buttress plate, resection of the aneurysm and repair of the aneurysm with inter-positional graft