Proximal humeral fractures, which occur mainly in older adults, account for approximately 4 to 5% of all fractures. Approximately 40% of these fractures are displaced fractures involving the surgical ...neck. Management of this group of fractures is often challenging and the outcome is frequently unsatisfactory. In particular it is not clear whether surgery gives better outcomes than non-surgical management. Currently there is much variation in the use of surgery and a lack of good quality evidence to inform this decision.
We aim to undertake a pragmatic UK-based multi-centre randomised controlled trial evaluating the effectiveness and cost-effectiveness of surgical versus standard non-surgical treatment for adults with an acute closed displaced fracture of the proximal humerus with involvement of the surgical neck. The choice of surgical intervention is left to the surgeon, who must use techniques that they are fully experienced with. This will avoid 'learning curve' problems. We will promote good standards of non-surgical care, similarly insisting on care-provider competence, and emphasize the need for comparable provision of rehabilitation for both groups of patients.We aim to recruit 250 patients from a minimum of 18 NHS trauma centres throughout the UK. These patients will be followed-up for 2 years. The primary outcome is the Oxford Shoulder Score, which will be collected via questionnaires completed by the trial participants at 6, 12 and 24 months. This is a 12-item condition-specific questionnaire providing a total score based on the person's subjective assessment of pain and activities of daily living impairment. We will also collect data for other outcomes, including general health measures and complications, and for an economic evaluation. Additionally, we plan a systematic collection of reasons for non-inclusion of eligible patients who were not recruited into the trial, and their baseline characteristics, treatment preferences and intended treatment.
This article presents the protocol for a multi-centre randomised controlled trial. It gives extensive details of, and the basis for, the chosen methods, and describes the key measures taken to avoid bias and to ensure validity.
Current Controlled Trials ISRCTN50850043.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Bisphosphonate therapy reduces fracture risk and increases bone mineral density (BMD), primarily through inhibition of bone resorption. Regular exercise is also associated with reduced fracture risk ...but with smaller and less consistent increases in BMD. Properly prescribed exercise, however, can add to the benefits of bisphosphonates by increasing bone formation (osteogenesis) and bone strength, improving bone geometry (structural arrangement), and reducing fall risk and comorbidity, independent of BMD. Previous recommendations have underestimated the value of regular exercise for bone health by placing too much emphasis on BMD and not enough on other aspects of fracture risk.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Abstract
BACKGROUND: Incidence of glioma varies significantly by sex, and most glioma histologies occur with greater incidence in males. Previous analyses have examined the impact of estrogen ...exposure as a risk factor for these tumors, but have found results of varying significance and low effect size. There may be differences in effect of previously discovered risk alleles that contribute to sex differences.
METHODS: Using data collected for three previous glioma GWAS in European-ancestry populations (MD Anderson Cancer Center, the San Francisco Adult Glioma Study, and the Glioma International Case Control Study) we assessed sex-specific effects for 14 previously identified and 13 newly identified glioma risk SNPs (27 total) overall and for glioblastoma (GBM) and non-GBM tumors separately. There were 3,892 male cases (59% GBM), 4,522 male controls, 2,500 female cases (52% GBM) and 4,940 female controls. Sex-specific odds ratios (ORM and ORF), 95% confidence intervals (95% CI) and p values (pM and pF) were generated using stratified logistic regression models. Data from each study were analyzed separately and combined using inverse variance weighted meta-analysis. Results were considered statistically significant at p<6.2x10-4.
RESULTS: In GBM, rs11979158 (7p11.2, pM=1.01x10-10, ORM=1.43 95% CI: 1.28-1.59; pF=3.43x10-3, ORF=1.22 95% CI: 1.07-1.39) and rs2562152 (16p13.3, pM=5.59x10-4, ORM=1.23 95% CI: 1.09-1.39; pF=1.22x10-1, ORF=1.12 95% CI: 0.97-1.29) had significant effect in males only. In non-GBM gliomas, rs12076373 (1q44, pM=3.56x10-7, ORM=1.41, 95% CI: 1.23-1.61; pF=1.76x10-2, ORF=1.20, 95% CI: 1.03-1.38), rs11979158 (7p11.2, pM=1.77x10-5, ORM=1.32 95% CI: 1.16-1.49; pF=2.73x10-1, ORF=1.08 95% CI: 0.94-1.24), and rs3751667 (16p13.3, pM=9.44x10-7, ORM=1.30 95% CI: 1.17-1.44; pF=5.18x10-2, ORF=1.13 95% CI: 1.00-1.28) had significant effect in males only. Effect size for rs55705857 (8q24.21) varied significantly by sex, with ORM=2.63 (95% CI: 2.24-3.09, pM=4.42x10-32), as compared to ORF=3.95 (95% CI: 3.28-4.76, pF=1.82x10-47). A sensitivity analysis was performed due to allele frequency heterogeneity by study and results did not change.
CONCLUSIONS: Sex differences and other demographic differences in cancer susceptibility can provide important clues to etiology, and these differences can be leveraged for discovery in genetic association studies. Significant differences in effect size may suggest variation in genetic effect of risk alleles or in unidentified risk factors that vary in prevalence or effect by sex. There may also be differences in the distribution of molecular subtypes within each histology by sex. Further investigation using an agnostic approach may further elucidate the relationship between effect of risk alleles and sex.
Citation Format: Quinn T. Ostrom, Ben Kinnersley, Margaret Wrensch, Jeanette E. Eckel-Passow, Georgina Armstrong, Terri Rice, Yanwen Chen, John Wiencke, Lucie McCoy, Helen Hansen, Christopher Amos, Jonine L. Bernstein, Elizabeth B. Claus, Dora Il'yasova, Christoffer Johansen, Daniel Lachance, Rose Lai, Ching C. Lau, Ryan T. Merrell, Sara H. Olson, Siegal Sadetzki, Joellen Schildkraut, Sanjay Shete, Richard S. Houlston, Robert B. Jenkins, Beatrice Melin, Melissa Bondy, Jill S. Barnholtz-Sloan. Estimating sex-specific effects of genetic loci associated with glioma risk abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1315. doi:10.1158/1538-7445.AM2017-1315
Northern blue whiting is a small abundant pelagic gadoid that is widely distributed in the northeast Atlantic and one of the most commercially valuable species west of the British Isles and Ireland. ...Over the last two decades the northeast Atlantic stock has undergone dramatic changes in abundance. The stock size decreased dramatically from 2007 to 2011, but has since shown signs of recovery. Changes in recruitment levels have occurred almost simultaneously with unusual changes in the north Atlantic ecosystem and oceanography. These links may suggest a causal linkage and the possibility of improving our understanding of the recruitment and spawning stock distribution. Here we use a set of geostatistical indices to describe the temporal and spatial patterns of the northeast Atlantic blue whiting stock in spring of 2006–2014. Geostatistical indices were computed to investigate changes in the spatial distribution, dynamics and variability of the stock in terms of density and location. Indices revealed 3 different distribution patterns over the time series. Main concentrations were either found around Rockall (first years), west of the Hebrides (2008–2013) or in the southern survey area (2014). The distribution was found to be age structured, with young blue whiting mainly concentrated in shallower areas (<1000m), along the shelf edge, and older specimens being more prominent in deeper waters (>1000m). A general additive mixed model (GAMM) was used to model the distribution of blue whiting according to environmental conditions and location.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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