Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

Comparison On Compressive Behaviour Of Aluminium Honeycomb And Titanium Alloy Micro Lattice Blocks

The paper discusses the compressive behaviour of two materials, the conventional aluminium honeycomb and the new titanium alloy micro lattice blocks. The new titanium alloy micro lattice structure is being developed as core material candidate in sandwich construction for aerospace application. Experimental tests have been done on the blocks in order to compare its property with the aluminium honeycomb. Compression strength as well as compressive behaviour of both materials are compared and observed. The mechanisms that contributed to the differences in their performance are discussed and this will be used to improve the geometrical and structural design of micro lattice structure in order to achieve properties that are superior or at least comparable with that of aluminium honeycomb

Nerve-sparing radical abdominal trachelectomy versus nerve-sparing radical hysterectomy in early-stage (FIGO IA2-IB) cervical cancer: A comparative study on feasibility and outcome

Objectives: Standard treatment in early-stage cervical cancer is a radical hysterectomy (RH) with pelvic lymphadenectomy. In women who wish to preserve fertility radical vaginal trachelectomy has been proposed; however, this is not feasible in larger tumors, and nervesparing surgery is not possible. Nerve-sparing radical abdominal trachelectomy (NSRAT) overcomes these disadvantages. Methods: Case-control study of women with early-stage cervical cancer (International Federation of Gynecology and Obstetrics IA2-IB) submitted to NSRAT from 2000 until 2011.Women submitted to nerve-sparing RH with early-stage cervical cancer were included as control subjects. Results: Twenty-eight patients and 77 control subjects were included. Neoadjuvant chemotherapy was administered in 3 women before NSRAT because the linear extension was or exceeded 40mm. Local recurrence ratewas 3.6%(95% confidence interval [CI], 0.00-10.6) in the NSRAT group compared with 7.8% (95% CI, 1.7-13.9) in the control group (P = 0.44). No significant difference was found between both groups regarding disease-free survival and survival. The overall pregnancy rate was 52.9% (95% CI, 28.7%-77.2%). The mean follow-up was 47.3 months (range, 6-122 months) for NSRAT and 51.8 months (11-129.6 months) for nerve-sparing RH. Conclusions: Nerve-sparing radical abdominal trachelectomy seems safe and effective in women with early-stage cervical cancer who wish to preserve fertility. Respective women should be informed about this treatment option, especially if the tumor is too large for radical vaginal trachelectomy

Machine learning techniques applied to prediction of residual strength of clay

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil