J. Chem. SOC. Chem. Commun. 1990, 581. [8] SiO was synthesized by passing 0, over hot silicon (1200°C); Pd atoms were generated by vaporization of the metal at 1450 "C. The cryostat and vaporization conditions have been described previously: R. Ahlrichs, R. Becherer, M. Binnewies, H. Borrmann, M. Lakenbrink, S. Schunck, H. Schnockel, 1 Am. Chem. SOC. 1986,108,7905. The IR spectra were recorded on an IFS-113 v and an IFS-66v FT-IR spectrometer in absorption with the help of a reflection unit.
Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities helps us better understand patients’ needs and interest in health-related knowledge. However, the statistical topic analysis employed in previous studies is becoming impractical for processing the rapidly increasing amount of online data. Automatic topic detection based on document clustering is an alternative approach for extracting health-related hot topics in online communities. In addition to the keyword-based features used in traditional text clustering, we integrate medical domain-specific features to represent the messages posted in online health communities. Three disease discussion boards, including boards devoted to lung cancer, breast cancer and diabetes, from an online health community are used to test the effectiveness of topic detection. Experiment results demonstrate that health-related hot topics primarily include symptoms, examinations, drugs, procedures and complications. Further analysis reveals that there also exist some significant differences among the hot topics discussed on different types of disease discussion boards.
Comparing the clinical and radiographic outcomes in anterior cervical discectomy and fusion (ACDF) using a zero-profile anchored spacer (ROI-C) or a conventional cage-plate construct (CPC) for treating noncontiguous bilevel of cervical degenerative disc disease (CDDD).Overall, 46 patients with 2 noncontiguous segments of CDDD, treated with ACDF from January 2011 to October 2015, were included in this study. ROI-C was used in 22 patients (group A) and CPC in 24 patients (group B). The clinical and radiographic outcomes and complications were compared pre- and postoperatively. All patients were followed up for at least 24 months after surgery.No significant difference was found in fusion rate, cervical curvature, height of fused segment (FSDH), intraoperative blood loss, and Japanese Orthopaedic Association (JOA), and Neck Disability Index (NDI) scores between the 2 groups. Group A had a shorter operation time and significantly lower incidence of dysphagia (3 and 24 months postoperatively) than group B (P < .001 and P < .05, respectively). Moreover, group A had a higher loss of FSDH than group B, but with no difference between the 2 groups (P > .05). Two cages developed subsidence in group A (4.5%) and 2 adjacent levels developed degeneration in group B (2,8%).ACDF with ROI-C device was superior to CPC for noncontiguous bilevel of CDDD because it avoided postoperative dysphagia and required a shorter operation time. Moreover, the clinical outcomes were comparable. Prospective trials with larger samples and longer follow-up are required to confirm the results.
BackgroundHealth care social media used for health information exchange and emotional communication involves different types of users, including patients, caregivers, and health professionals. However, it is difficult to identify different stakeholders because user identification data are lacking due to privacy protection and proprietary interests. Therefore, identifying the concerns of different stakeholders and how they use health care social media when confronted with huge amounts of health-related messages posted by users is a critical problem.ObjectiveWe aimed to develop a new content analysis method using text mining techniques applied in health care social media to (1) identify different health care stakeholders, (2) determine hot topics of concern, and (3) measure sentiment expression by different stakeholders.MethodsWe collected 138,161 messages posted by 39,606 members in lung cancer, diabetes, and breast cancer forums in the online community MedHelp.org over 10 years (January 2007 to October 2016) as experimental data. We used text mining techniques to process text data to identify different stakeholders and determine health-related hot topics, and then analyzed sentiment expression.ResultsWe identified 3 significantly different stakeholder groups using expectation maximization clustering (3 performance metrics: Rand=0.802, Jaccard=0.393, Fowlkes-Mallows=0.537; P<.001), in which patients (24,429/39,606, 61.68%) and caregivers (12,232/39,606, 30.88%) represented the majority of the population, in contrast to specialists (2945/39,606, 7.43%). We identified 5 significantly different health-related topics: symptom, examination, drug, procedure, and complication (Rand=0.783, Jaccard=0.369, Fowlkes-Mallows=0.495; P<.001). Patients were concerned most about symptom topics related to lung cancer (536/1657, 32.34%), drug topics related to diabetes (1883/5904, 31.89%), and examination topics related to breast cancer (8728/23,934, 36.47%). By comparison, caregivers were more concerned about drug topics related to lung cancer (300/2721, 11.03% vs 109/1657, 6.58%), procedure topics related to breast cancer (3952/13,954, 28.32% vs 5822/23,934, 24.33%), and complication topics (4449/25,701, 17.31% vs 4070/31,495, 12.92%). In addition, patients (9040/36,081, 25.05%) were more likely than caregivers (2659/18,470, 14.39%) and specialists (17,943/83,610, 21.46%) to express their emotions. However, patients’ sentiment intensity score (2.46) was lower than those of caregivers (4.66) and specialists (5.14). In particular, for caregivers, negative sentiment scores were higher than positive scores (2.56 vs 2.18), with the opposite among specialists (2.62 vs 2.46). Overall, the proportion of negative messages was greater than that of positive messages related to symptom, complication, and examination. The pattern was opposite for drug and procedure topics. A trend analysis showed that patients and caregivers gradually changed their emotional state in a positive direction.ConclusionsThe hot topics of interest and sentiment expr...
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