Five-year publication rate of clinical presentations at the open and closed American shoulder and elbow surgeons annual meeting from 2005–2010

© 2016, The Author(s). Background: The purpose of this study was to evaluate the five-year publication rate of papers presented at both the open and closed American Shoulder and Elbow Surgeons’ (ASES) annual meetings from 2005 to 2010. Methods: Online abstracts of the presentations at the open and closed ASES annual meetings were independently screened for clinical studies and graded for quality using level of evidence. The databases PubMed (MEDLINE), Ovid (MEDLINE), and EMBASE were comprehensively searched for full-text publications corresponding to these presentations and any paper published within five years of the presentation date was counted. Results: Overall, 131/266 papers corresponding to the meeting presentations were identified for a five-year publication rate of 49.2 %. Sixty two (48 %) of the papers were published in The Journal of Shoulder and Elbow Surgeons, 23 (18 %) were published in The American Journal of Sports Medicine, and 20 (16 %) were published in The Journal of Bone and Joint Surgery. The mean patient sample size included in presentations with a subsequent full-text publication was higher (154; standard error =27) than the presentations not published (93; standard error = 13) (p = 0.039). There was no correlation (p = 0.248) between the publication rate and the level of evidence of the presentations. Conclusions: The publication rate of presentations at ASES meetings from 2005 to 2010 is similar to that reported from other orthopaedic meetings. Studies with large sample sizes should continue to be encouraged, and high quality presentations must consistently be followed up with full-text manuscript preparation in order to maximize the future clinical impact

Glaucoma diagnosis using multi-feature analysis and a deep learning technique

AbstractIn this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.</jats:p

Crashworthiness simulation of composite automotive structures

In 1990 the Automotive Composites Consortium (ACC) began the investigation of crash worthiness simulation methods for composite materials. A contract was given to Livermore Software Technology Corporation (LSTC) to implement a new damage model in LS-DYNA3DTM specifically for composite structures. This model is in LS-DYNA3DTM and is in use by the ACC partners. In 1994 USCAR, a partnership of American auto companies, entered into a partnership called SCAAP (Super Computing Automotive Applications Partnership) for the express purpose of working with the National Labs on computational oriented research. A CRADA (Cooperative Research and Development Agreement) was signed with Lawrence Livermore National Laboratory, Oak Ridge National Laboratory, Sandia National Laboratory, Argonne National Laboratory, and Los Alamos National Laboratory to work in three distinctly different technical areas, one of which was composites material modeling for crash worthiness. Each Laboratory was assigned a specific modeling task. The ACC was responsible for the technical direction of the composites project and provided all test data for code verification. All new models were to be implemented in DYNA3D and periodically distributed to all partners for testing. Several new models have been developed and implemented. Excellent agreement has been shown between tube crush simulation and experiments

Health care in Bosnia and Herzegovina before, during, and after 1992–1995 war: a personal testimony

Market-based health care reform during democratic transition in Bosnia and Herzegovina was complicated by the 1992–1995 war, that devastated the country and greater part of its health care infrastructure. The course of the transition and consequences of war for the health system and health professionals are presented here from the perspective of the author. The description of real-life situations and their context is used to illustrate the problems physicians, as well as international community, were faced with and how they tried to cope with them during and after the war. Speaking openly about the mistakes that were made in those times is the first step in preventing them from happening again and an invitation for exchange of opinions and open academic discussion

Globular Cluster UVIT legacy Survey (GlobUleS) III. Omega Centauri in Far-Ultraviolet

We present the first comprehensive study of the most massive globular cluster Omega Centauri in the far-ultraviolet (FUV) extending from the center to ~ 28% of the tidal radius using the Ultraviolet Imaging Telescope aboard AstroSat. A comparison of the FUV-optical color-magnitude diagrams with available canonical models reveals that the horizontal branch (HB) stars bluer than the knee (hHBs) and the white dwarfs (WDs) are fainter in the FUV by ~ 0.5 mag than model predictions. They are also fainter than their counterparts in M13, another massive cluster. We simulated HB with at least five subpopulations, including three He-rich populations with a substantial He enrichment of Y up to 0.43 dex, to reproduce the observed FUV distribution. We find the He-rich younger subpopulations to be radially more segregated than the He-normal older ones, suggesting an in-situ enrichment from older generations. The Omega Cen hHBs span the same effective temperature range as their M13 counterparts, but some have smaller radii and lower luminosities. This may suggest that a fraction of Omega Cen hHBs are less massive than those of M13, similar to the result derived from earlier spectroscopic studies of outer extreme HB stars. The WDs in Omega Cen and M13 have similar luminosity-radius-effective temperature parameters, and 0.44 - 0.46 M$_\odot$ He-core WD model tracks evolving from progenitors with Y = 0.4 dex are found to fit the majority of these. This study provides constraints on the formation models of Omega Cen based on the estimated range in age, [Fe/H] and Y (in particular), for the HB stars.Comment: Accepted for publication in ApJL; 13 pages, 5 figures, 1 tabl

Time from first presentation in primary care to treatment of symptomatic colorectal cancer:effect on disease stage and survival

BACKGROUND: British 5-year survival from colorectal cancer (CRC) is below the European average, but the reasons are unclear. This study explored if longer provider delays (time from presentation to treatment) were associated with more advanced stage disease at diagnosis and poorer survival. METHODS: Data on 958 people with CRC were linked with the Scottish Cancer Registry, the Scottish Death Registry and the acute hospital discharge (SMR01) dataset. Time from first presentation in primary care to first treatment, disease stage at diagnosis and survival time from date of first presentation in primary care were determined. Logistic regression and Cox survival analyses, both with a restricted cubic spline, were used to model stage and survival, respectively, following sequential adjustment of patient and tumour factors. RESULTS: On univariate analysis, those with <4 weeks from first presentation in primary care to treatment had more advanced disease at diagnosis and the poorest prognosis. Treatment delays between 4 and 34 weeks were associated with earlier stage (with the lowest odds ratio occurring at 20 weeks) and better survival (with the lowest hazard ratio occurring at 16 weeks). Provider delays beyond 34 weeks were associated with more advanced disease at diagnosis, but not increased mortality. Following adjustment for patient, tumour factors, emergency admissions and symptoms and signs, no significant relationship between provider delay and stage at diagnosis or survival from CRC was found. CONCLUSIONS: Although allowing for a nonlinear relationship and important confounders, moderately long provider delays did not impact adversely on cancer outcomes. Delays are undesirable because they cause anxiety; this may be fuelled by government targets and health campaigns stressing the importance of very prompt cancer diagnosis. Our findings should reassure patients. They suggest that a health service's primary emphasis should be on quality and outcomes rather than on time to treatment