Background: Many previous papers have investigated most-cited articles or most productive authors in academics, but few have studied most-cited authors. Two challenges are faced in doing so, one of which is that some different authors will have the same name in the bibliometric data, and the second is that coauthors’ contributions are different in the article byline. No study has dealt with the matter of duplicate names in bibliometric data. Although betweenness centrality (BC) is one of the most popular degrees of density in social network analysis (SNA), few have applied the BC algorithm to interpret a network’s characteristics. A quantitative scheme must be used for calculating weighted author credits and then applying the metrics in comparison. Objective: This study aimed to apply the BC algorithm to examine possible identical names in a network and report the most-cited authors for a journal related to international mobile health (mHealth) research. Methods: We obtained 676 abstracts from Medline based on the keywords “JMIR mHealth and uHealth” (Journal) on June 30, 2018. The author names, countries/areas, and author-defined keywords were recorded. The BCs were then calculated for the following: (1) the most-cited authors displayed on Google Maps; (2) the geographical distribution of countries/areas for the first author; and (3) the keywords dispersed by BC and related to article topics in comparison on citation indices. Pajek software was used to yield the BC for each entity (or node). Bibliometric indices, including h-, g-, and x-indexes, the mean of core articles on g(Ag)=sum (citations on g-core/publications on g-core), and author impact factor (AIF), were applied. Results: We found that the most-cited author was Sherif M Badawy (from the United States), who had published six articles on JMIR mHealth and uHealth with high bibliometric indices (h=3; AIF=8.47; x=4.68; Ag=5.26). We also found that the two countries with the highest BC were the United States and the United Kingdom and that the two keyword clusters of mHealth and telemedicine earned the highest indices in comparison to other counterparts. All visual representations were successfully displayed on Google Maps. Conclusions: The most cited authors were selected using the authorship-weighted scheme (AWS), and the keywords of mHealth and telemedicine were more highly cited than other counterparts. The results on Google Maps are novel and unique as knowledge concept maps for understanding the feature of a journal. The research approaches used in this study (ie, BC and AWS) can be applied to other bibliometric analyses in the future.

Retrospective cohort study. To validate computed tomography (CT) radiodensity in Hounsfield units (HU) as a prognostic marker for pedicle screw loosening or cage subsidence in minimally invasive transforaminal lumbar interbody fusion (MI-TLIF). The retrospective study involved 198 patients treated with MI-TLIF. Screw loosening (SL), cage subsidence (CS), and fusion status were assessed by plain radiographs. The risk factors of SL and CS were identified using logistic regression. A total of 258 levels and 930 screws were analyzed. During a 2-year follow-up, 16.2% and 24.7% of patients had CS and SL respectively. The cut-off value of L1 HU for predicting SL or CS was 117. The L1 HU < 117 and BMI ≥ 25 were two independent risk factors. The risk of SL or CS was 4.1 fold in patients L1 HU < 117 and 2.6 fold in patients with BMI ≥ 25. For patients concurrently having BMI ≥ 25 and pre-op L1 HU < 117, the risk was 4.3 fold. Fusion rate and clinical outcome were comparable in patients with SL or CS. L1 HU < 117 and BMI > 25 were two independent risk factors that can be screened preoperatively for preventing SL or CS and lead to better management of patients undergoing MI-TLIF.

Background: Studies of the 100 most-cited articles are reported for many subjects. However, none has analyzed the article characteristics associated with high citation frequency. This study aims to (1) graphically depict characteristics of the 100 top-cited articles addressing adult spinal deformity (ASD), (2) diagram the association between articles according to subject and major topic medical subject headings (MeSHs), and (3) investigate whether major topic MeSH correlates with article citation frequency. Methods: The 100 top-cited ASD publications since 2011 were retrieved using a PubMed Central search on May 6, 2020. Using titles and abstracts, eight subject categories were identified: surgery, conservative treatment, normal values in spinopelvic alignment, review, cervical alignment, classification, compensatory mechanism, and spine-hip relationship. Sankey diagrams were used to organize the information. Network analysis was performed according to article subject and major topic MeSHs. Pearson’s r was used to determine whether the weighted number of citations correlates with major topic MeSHs and the number of citations. Results: The average number of citations per article was 34.8 (range, 19–156). The most represented country was USA (n = 51). The most productive and highly cited journal was Spine (Phila Pa 1976) (n = 34; average, 38.2 citations per article). The most frequent subject categories and major topic MeSHs were “surgery” (n = 53) and “scoliosis” (weighted count, 9.8), while articles with the subject “compensatory” had the highest average number of citations (64.7). The most highly cited article, by Dr. F. Schwab in 2012, had 156 citations. Network analysis revealed the relationships between these articles according to major topic MeSHs. The weighted number of citations according to major topic MeSHs correlated significantly with article citation frequency (Pearson’s r, 0.57; p < 0.001). Conclusion: Multiple characteristics of the 100 top-cited ASD articles are presented in diagrams to guide evidence-based clinical decision-making in ASD.

Background: Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute’s well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature. Objective: The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory–Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage. Methods: We recruited 1002 nurses working in a medical center in Taiwan to complete the Chinese version of the 20-item MBI-HSS in August 2016. The k-mean and CNN were used as unsupervised and supervised learnings for dividing nurses into two classes (n=531 and n=471 of suspicious BO+ and BO−, respectively) and building a BO predictive model to estimate 38 parameters. Data were separated into training and testing sets in a proportion 70%:30%, and the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve) across studies for comparison. An app predicting respondent BO was developed involving the model’s 38 estimated parameters for a website assessment. Results: We observed that (1) the 20-item model yields a higher accuracy rate (0.95) with an area under the curve of 0.97 (95% CI 0.94-0.95) based on the 1002 cases, (2) the scheme named matching personal response to adapt for the correct classification in model drives the prior model’s predictive accuracy at 100%, (3) the 700-case training set with 0.96 accuracy predicts the 302-case testing set reaching an accuracy of 0.91, and (4) an available MBI-HSS app for nurses predicting BO was successfully developed and demonstrated in this study. Conclusions: The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of nurse BO has been particularly demonstrated in Excel (Microsoft Corp). An app developed for helping nurses to self-assess job BO at an early stage is required for application in the future.

Purpose: A meta-analysis was conducted to evaluate the clinical outcomes, complications, reoperation rates, and late neurological deterioration between anterior decompression and fusion (ADF) and laminoplasty (LAMP) in the treatment of multilevel cervical ossification of the posterior longitudinal ligament (OPLL). Methods: All related studies published up to August 2015 were acquired by searching PubMed and EMBASE. Exclusion criteria were case reports, revision surgeries, combined anterior and posterior surgeries, the other posterior approaches including laminectomy or laminectomy and instrumented fusion, non-English studies, and studies with quality assessment scores of <7. The main end points including Japanese Orthopedic Association (JOA) score, recovery rate of JOA, cervical lordosis, complication rate, reoperation rate, and late neurological deterioration were analyzed. All available data was analyzed using RevMan 5.2.0 and Stata 12.0. Results: A total of seven studies were included in the meta-analysis. The mean surgical level of ADF was 3.1, and the mean preoperative occupation ratios of ADF and LAMP group were 55.9% and 51.9%, respectively. No statistical difference was observed with regard to preoperative occupation ratio and preoperative JOA score. Although LAMP group had a higher preoperative cervical lordosis than ADF group (P<0.05, weighted mean difference [WMD] =-5.73, 95% confidence interval [CI] =–9.67––1.80), significantly decreased cervical lordosis was observed in LAMP group after operation. ADF group had higher postoperative JOA score (P<0.05, WMD =2.18, 95% CI =0.98–3.38) and neurological recovery rate (P<0.05, WMD =27.22, 95% CI =15.20–39.23). Furthermore, ADF group had a lower late neurological deterioration rate than the LAMP group (P<0.05, risk difference =0.16, 95% CI =0.04–0.73). The complication rates of both groups had no statistical difference. However, LAMP group had a significantly lower reoperation rate than ADF group. The reoperation rate of ADF group (20.5%) was almost six times that of LAMP group (3.5%). Conclusion: Our meta-analysis suggested that ADF was associated with better postoperative neurological function, neurological recovery rate, and less late neurological deterioration than LAMP in the treatment of multilevel cervical OPLL with a high mean occupation ratio. LAMP was associated with a decreased postoperative cervical lordosis, which might be a cause of late neurological deterioration. The complication rates of both groups showed no statistical difference. However, the reoperation rate was significantly higher in ADF group compared with LAMP group. Benefits and risks should be balanced when ADF or LAMP is selected.

Study Design: Retrospective case-control study. Objectives: This study aims to present the clinical and radiographical outcomes of the titanium-polyetheretherketone (Ti/PEEK) composite cage compared to those of the standard PEEK cage in patients receiving minimally invasive transforaminal lumbar interbody fusion (MI-TLIF). Methods: Patients receiving 1 level MI-TLIF between October 2015 and October 2017 were included with a minimum of 2-year follow-up. The patients were segregated into 2 groups; Ti/PEEK group and PEEK group. Each patient was propensity-matched using preoperative age, sex, and body mass index. Early fusion rate was evaluated by computed tomography at postoperative 6 months. Clinical outcomes were assessed using the visual analog scale (VAS) and Oswestry Disability Index (ODI) scores. Results: After matching, there were 27 patients included in each group. The demographics, diagnosis, and surgical details were not significantly different between the 2 groups. The 6-month rate was 88.9% in Ti/PEEK group. The fusion rate and cage subsidence rate had no difference between the 2 groups. The complication rate in the Ti/PEEK group was comparable to that in the PEEK group. There was no difference in VAS and ODI scores during a 2-year follow-up period. Conclusions: The use of Ti/PEEK composite cage was as safe and effective as the use of PEEK cage in MI-TLIF. The 6-month fusion rate was 88.9%. Our finding revealed comparable clinical results for surgeons using Ti/PEEK composite cages in MI-TLIF compared to those using the PEEK cage.