Using a motorcycle rider behaviour questionnaire (MRBQ) to investigate the predictors of motorcyclists' crash risk

In 2005, there were over 23,000 motorcyclists (including moped riders) involved in injury road traffic crashes in Great Britain and 6,142 of these riders were killed or seriously injured (Department for Transport, 2006). In order to reduce the casualty rates it is necessary to understand why motorcycle crashes happen. The present study approached this issue by investigating the role of aberrant rider behaviour, using a Motorcycle Rider Behaviour Questionnaire (MRBQ) as a framework. The aims of the study were to test the reliability and discriminant validity of the MRBQ and to examine which MRBQ behaviours relate to crash risk. Following the Driver Behaviour Questionnaire (e.g., Reason et al., 1990), which classifies driver behaviour into a system of errors ('the failure of planned actions to achieve their intended consequences') and violations ('deliberate deviations from those practices necessary to maintain the safe operation of a potentially hazardous system'), the MRBQ measured errors and violations, but with regard to motorcycling rather than car driving (see Elliott, Baughan & Sexton, 2007). The questionnaire consisted of 43 items to measure the self-reported frequency of specific riding behaviours. The questionnaire was administered to a sample of motorcyclists via a postal survey (N = 8,666). Principal components analysis revealed a 5-factor solution (TRAFFIC ERRORS, CONTROL ERRORS, SPEED VIOLATIONS, performance of STUNTS, and use of SAFETY EQUIPMENT). Generalised linear modelling showed that, while controlling for the effects of age, experience and annual mileage, TRAFFIC ERRORS were the main predictors of crash risk. For crashes in which respondents accepted some degree of blame, CONTROL ERRORS and SPEED VIOLATIONS were also significant predictors of crash risk. Implications of the findings will be discussed in relation to deciding on which countermeasures may be most effective at reducing motorcycle casualty rates

Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: A pilot study of a potential cancer field effect

Purpose: In women with biopsy-proven breast cancer, histologically normal areas of the parenchyma have shown molecular similarity to the tumor, supporting a potential cancer field effect. The purpose of this work was to investigate relationships of human-engineered radiomic and deep learning features between regions across the breast in mammographic parenchymal patterns and specimen radiographs. Approach: This study included mammograms from 74 patients with at least 1 identified malignant tumor, of whom 32 also possessed intraoperative radiographs of mastectomy specimens. Mammograms were acquired with a Hologic system and specimen radiographs were acquired with a Fujifilm imaging system. All images were retrospectively collected under an Institutional Review Board-approved protocol. Regions of interest (ROI) of 128 × 128 pixels were selected from three regions: within the identified tumor, near to the tumor, and far from the tumor. Radiographic texture analysis was used to extract 45 radiomic features and transfer learning was used to extract 20 deep learning features in each region. Kendall’s Tau-b and Pearson correlation tests were performed to assess relationships between features in each region. Results: Statistically significant correlations in select subgroups of features with tumor, near to the tumor, and far from the tumor ROI regions were identified in both mammograms and specimen radiographs. Intensity-based features were found to show significant correlations with ROI regions across both modalities. Conclusions: Results support our hypothesis of a potential cancer field effect, accessible radiographically, across tumor and non-tumor regions, thus indicating the potential for computerized analysis of mammographic parenchymal patterns to predict breast cancer risk.</p

Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons

Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help address the COVID-19 pandemic. The purpose of this study was to quantify longitudinal representativeness of the demographic characteristics of the primary MIDRC dataset compared to the United States general population (US Census) and COVID-19 positive case counts from the Centers for Disease Control and Prevention (CDC). Approach: The Jensen-Shannon distance (JSD), a measure of similarity of two distributions, was used to longitudinally measure the representativeness of the distribution of (1) all unique patients in the MIDRC data to the 2020 US Census and (2) all unique COVID-19 positive patients in the MIDRC data to the case counts reported by the CDC. The distributions were evaluated in the demographic categories of age at index, sex, race, ethnicity, and the combination of race and ethnicity. Results: Representativeness of the MIDRC data by ethnicity and the combination of race and ethnicity was impacted by the percentage of CDC case counts for which this was not reported. The distributions by sex and race have retained their level of representativeness over time. Conclusion: The representativeness of the open medical imaging datasets in the curated public data commons at MIDRC has evolved over time as the number of contributing institutions and overall number of subjects have grown. The use of metrics, such as the JSD support measurement of representativeness, is one step needed for fair and generalizable AI algorithm development.</p

General practice performance in referral for suspected cancer: influence of number of cases and case-mix on publicly reported data

Background:Publicly available data show variation in GPs’ use of urgent suspected cancer (USC) referral pathways. We investigated whether this could be due to small numbers of cancer cases and random case-mix, rather than due to true variation in performance. Methods:We analysed individual GP practice USC referral detection rates (proportion of the practice's cancer cases that are detected via USC) and conversion rates (proportion of the practice's USC referrals that prove to be cancer) in routinely collected data from GP practices in all of England (over 4 years) and northeast Scotland (over 7 years). We explored the effect of pooling data. We then modelled the effects of adding random case-mix to practice variation. Results:Correlations between practice detection rate and conversion rate became less positive when data were aggregated over several years. Adding random case-mix to between-practice variation indicated that the median proportion of poorly performing practices correctly identified after 25 cancer cases were examined was 20% (IQR 17 to 24) and after 100 cases was 44% (IQR 40 to 47). Conclusions:Much apparent variation in GPs’ use of suspected cancer referral pathways can be attributed to random case-mix. The methods currently used to assess the quality of GP-suspected cancer referral performance, and to compare individual practices, are misleading. These should no longer be used, and more appropriate and robust methods should be develope

Development of a Single Vector System that Enhances Trans-Splicing of SMN2 Transcripts

RNA modalities are developing as a powerful means to re-direct pathogenic pre-mRNA splicing events. Improving the efficiency of these molecules in vivo is critical as they move towards clinical applications. Spinal muscular atrophy (SMA) is caused by loss of SMN1. A nearly identical copy gene called SMN2 produces low levels of functional protein due to alternative splicing. We previously reported a trans-splicing RNA (tsRNA) that re-directed SMN2 splicing. Now we show that reducing the competition between endogenous splices sites enhanced the efficiency of trans-splicing. A single vector system was developed that expressed the SMN tsRNA and a splice-site blocking antisense (ASO-tsRNA). The ASO-tsRNA vector significantly elevated SMN levels in primary SMA patient fibroblasts, within the central nervous system of SMA mice and increased SMN-dependent in vitro snRNP assembly. These results demonstrate that the ASO-tsRNA strategy provides insight into the trans-splicing mechanism and a means of significantly enhancing trans-splicing activity in vivo

Randomized controlled trial of live lactobacillus acidophilus plus bifidobacterium bifidum in prophylaxis of diarrhea during radiotherapy in cervical cancer patients

<p>Abstract</p> <p>Background</p> <p>Radiation-induced diarrhea is frequently observed during pelvic radiotherapy. This study was performed to determine the ability of a probiotic containing live lactobacillus acidophilus plus bifidobacterium bifidum to reduce the incidence of radiation-induced diarrhea in locally advanced cervical cancer patients.</p> <p>Methods</p> <p>Patients who were undergoing pelvic radiotherapy concurrent with weekly cisplatin were randomly assigned to a study drug or placebo, in a double-blind study. Diarrhea was graded weekly according the Common Toxicity Criteria (CTC) system. Stool consistency and white and red blood cell count in stool were also assessed. The primary endpoint was to reduce the incidence of diarrhea, defined by a CTC grade 2 or more, and the need for anti-diarrheal medication.</p> <p>Results</p> <p>A total of 63 patients were enrolled. Grade 2 -3 diarrhea was observed in 45% of the placebo group (n = 31) and 9% of the study drug group (n = 32) (p = 0.002). Anti-diarrheal medication use was significantly reduced in the placebo group (p = 0.03). The patients in the study drug group had a significantly improved stool consistency (p < 0.001).</p> <p>Conclusions</p> <p>Live lactobacillus acidophilus plus bifidobacterium bifidum reduced the incidence of radiation-induced diarrhea and the need for anti-diarrheal medication and had a significant benefits on stool consistency.</p

The National Awareness and Early Diagnosis Initiative in England: assembling the evidence

A National Awareness and Early Diagnosis Initiative (NAEDI) has been established in England as part of the Government's strategy to improve cancer outcomes. One of the early priorities for this initiative has been to assemble the diverse evidence linking late diagnosis with poor survival and avoidable deaths. This supplement brings together new perspectives on existing research in this area together with findings from recently commissioned research. This paper describes a provisional model, the ‘NAEDI pathway', for testing hypotheses relating to late diagnosis and its impact. Key findings from other papers in this supplement are also highlighted

Detection of Melanoma Nodal Metastases; Differences in Detection Between Elderly and Younger Patients Do Not Affect Survival

Background. Melanoma lymph nodes metastases may be detected by patients or by physicians. Understanding the outcomes of self-detection or physician detection is essential for the design of follow-up studies. We evaluated the role of the method of detection in nodal disease in the prognosis of melanoma patients who underwent therapeutic lymph node dissection (TLND). Materials and Methods. All melanoma patients with palpable lymph nodes were included in a prospective database (n = 98), and the method of detection was recorded. Detection of lymph node metastases compared with pathological findings in the TLND was assessed by multivariate logistic regression. Disease-free survival (DFS) and disease-specific survival (DSS) were assessed by univariate and multivariate Cox proportional hazard analysis. Results. Nodal metastases were detected by physicians in 45% and by patients in 55% (P <0.001). Age was significantly associated with method of detection. Patients 60 years (odds ratio [OR] 0.3; P = 0.007). However, this was not associated with prognostic findings in TLND, number of positive nodes, tumor size, or extranodal spread. Method of detection or age at the time of nodal metastases was not significantly associated with 2-year DFS or DSS. Conclusions. 45% of all lymph node metastases in stage I-II melanoma patients are physician detected. Younger patients detect their own lymph node metastases significantly more often than elderly patients. However, neither the method of detection nor age correlates with DSS. More frequent follow-up would not alter DFS and DSS significantly