Measurement of the W mass with the ATLAS detector

We investigate the posibility of improving the W mass measurement at ATLAS. Given the high statistics of both W and Z bosons expected at the LHC, we estimate that a precision of 7 MeV per channel can be reached with 10 fb-1 of data.Comment: Poster presented at ICHEP08, Philadelphia, USA, July 2008. 3 pages, LaTeX, 2 eps figure

Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA).METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS &gt; 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values.RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication.CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.</p

Altered self-recognition in patients with schizophrenia

Abstract Self-alienation is a common characterization of various disturbing experiences in patients with schizophrenia. A vivid example comes from patient reports of not recognizing themselves when inspecting their specular image in the mirror. By applying the multisensory paradigm of the Enfacement Illusion, this study empirically addresses the specular Self-Other discrimination in patients with schizophrenia. 35 patients diagnosed with schizophrenia and 35 healthy matched controls were enrolled in the study. Results found that the group of patients with schizophrenia had a significant skewed self-other discrimination towards the other at baseline. Furthermore, the effect of visuo-tactile stimulation on self-recognition in the schizophrenia patients was significantly altered after both synchronous and asynchronous stimulation compared to baseline. This contrasted with healthy controls which in line with earlier studies only had significantly different self-recognition after synchronous stimulation. The study thus suggests that patients with schizophrenia have deviations in their specular self-recognition compared to healthy controls. Moreover, that temporal factors in multisensory integration may contribute to alterations of self-related stimuli in patients with schizophrenia