A population-based case-control study on social factors and risk of testicular germ cell tumours

Objectives Incidence rates for testicular cancer have risen over the last few decades. Findings of an association between the risk of testicular cancer and social factors are controversial. The association of testicular cancer and different indicators of social factors were examined in this study.<p></p> Design Case–control study.<p></p> Setting Population-based multicentre study in four German regions (city states Bremen and Hamburg, the Saarland region and the city of Essen).<p></p> Participants The study included 797 control participants and 266 participants newly diagnosed with testicular cancer of which 167 cases were classified as seminoma and 99 as non-seminoma. The age of study participants ranged from 15 to 69 years.<p></p> Methods Social position was classified by educational attainment level, posteducational training, occupational sectors according to Erikson-Goldthorpe-Portocarrero (EGP) and the socioeconomic status (SES) on the basis of the International SocioEconomic Index of occupational status (ISEI). ORs and corresponding 95% CIs (95% CIs) were calculated for the whole study sample and for seminoma and non-seminoma separately.<p></p> Results Testicular cancer risk was modestly increased among participants with an apprenticeship (OR=1.7 (95% CI 1.0 to 2.8)) or a university degree (OR=1.6 (95% CI 0.9 to 2.8)) relative to those whose education was limited to school. Analysis of occupational sectors revealed an excess risk for farmers and farm-related occupations. No clear trend was observed for the analyses according to the ISEI-scale.<p></p> Conclusions Social factors based on occupational measures were not a risk factor for testicular cancer in this study. The elevated risk in farmers and farm-related occupations warrants further research including analysis of occupational exposures.<p></p&gt

The sequential trauma score - a new instrument for the sequential mortality prediction in major trauma*

<p>Abstract</p> <p>Background</p> <p>There are several well established scores for the assessment of the prognosis of major trauma patients that all have in common that they can be calculated at the earliest during intensive care unit stay. We intended to develop a sequential trauma score (STS) that allows prognosis at several early stages based on the information that is available at a particular time.</p> <p>Study design</p> <p>In a retrospective, multicenter study using data derived from the Trauma Registry of the German Trauma Society (2002-2006), we identified the most relevant prognostic factors from the patients basic data (P), prehospital phase (A), early (B1), and late (B2) trauma room phase. Univariate and logistic regression models as well as score quality criteria and the explanatory power have been calculated.</p> <p>Results</p> <p>A total of 2,354 patients with complete data were identified. From the patients basic data (P), logistic regression showed that age was a significant predictor of survival (AUC_{model p}, area under the curve = 0.63). Logistic regression of the prehospital data (A) showed that blood pressure, pulse rate, Glasgow coma scale (GCS), and anisocoria were significant predictors (AUC_{model A}= 0.76; AUC_{model P + A}= 0.82). Logistic regression of the early trauma room phase (B1) showed that peripheral oxygen saturation, GCS, anisocoria, base excess, and thromboplastin time to be significant predictors of survival (AUC_{model B1}= 0.78; AUC_{model P +A + B1}= 0.85). Multivariate analysis of the late trauma room phase (B2) detected cardiac massage, abbreviated injury score (AIS) of the head ≥ 3, the maximum AIS, the need for transfusion or massive blood transfusion, to be the most important predictors (AUCmodel B2 = 0.84; AUCfinal model P + A + B1 + B2 = 0.90). The explanatory power - a tool for the assessment of the relative impact of each segment to mortality - is 25% for P, 7% for A, 17% for B1 and 51% for B2. A spreadsheet for the easy calculation of the sequential trauma score is available at: <url>http://www.sequential-trauma-score.com</url></p> <p>Conclusions</p> <p>This score is the first sequential, dynamic score to provide a prognosis for patients with blunt major trauma at several points in time. With every additional piece of information the precision increases. The medical team has a simple, useful tool to identify patients at high risk and to predict the prognosis of an individual patient with major trauma very early, quickly and precisely.</p

Charge carrier injection into insulating media: single-particle versus mean-field approach

Self-consistent, mean-field description of charge injection into a dielectric medium is modified to account for discreteness of charge carriers. The improved scheme includes both the Schottky barrier lowering due to the individual image charge and the barrier change due to the field penetration into the injecting electrode that ensures validity of the model at both high and low injection rates including the barrier dominated and the space-charge dominated regimes. Comparison of the theory with experiment on an unipolar ITO/PPV/Au-device is presented.Comment: 32 pages, 9 figures; revised version accepted to PR

Duration and predictors of emergency surgical operations - basis for medical management of mass casualty incidents

<p>Abstract</p> <p>Background</p> <p>Hospitals have a critically important role in the management of mass causality incidents (MCI), yet there is little information to assist emergency planners. A significantly limiting factor of a hospital's capability to treat those affected is its surgical capacity. We therefore intended to provide data about the duration and predictors of life saving operations.</p> <p>Methods</p> <p>The data of 20,815 predominantly blunt trauma patients recorded in the Trauma Registry of the German-Trauma-Society was retrospectively analyzed to calculate the duration of life-saving operations as well as their predictors. Inclusion criteria were an ISS ≥ 16 and the performance of relevant ICPM-coded procedures within 6 h of admission.</p> <p>Results</p> <p>From 1,228 patients fulfilling the inclusion criteria 1,793 operations could be identified as life-saving operations. Acute injuries to the abdomen accounted for 54.1% followed by head injuries (26.3%), pelvic injuries (11.5%), thoracic injuries (5.0%) and major amputations (3.1%). The mean cut to suture time was 130 min (IQR 65-165 min). Logistic regression revealed 8 variables associated with an emergency operation: AIS of abdomen ≥ 3 (OR 4,00), ISS ≥ 35 (OR 2,94), hemoglobin level ≤ 8 mg/dL (OR 1,40), pulse rate on hospital admission < 40 or > 120/min (OR 1,39), blood pressure on hospital admission < 90 mmHg (OR 1,35), prehospital infusion volume ≥ 2000 ml (OR 1,34), GCS ≤ 8 (OR 1,32) and anisocoria (OR 1,28) on-scene.</p> <p>Conclusions</p> <p>The mean operation time of 130 min calculated for emergency life-saving surgical operations provides a realistic guideline for the prospective treatment capacity which can be estimated and projected into an actual incident admission capacity. Knowledge of predictive factors for life-saving emergency operations helps to identify those patients that need most urgent operative treatment in case of blunt MCI.</p