Weak convergence of probability measures on product spaces with applications to sums of random vectors

Weak convergence of probability measures on product spaces with applications to sums of random vector

Diffusion approximations in collective risk theory

Collective risk theory concerned with random fluctuations of total assets of insurance compan

A primary care, multi-disciplinary disease management program for opioid-treated patients with chronic non-cancer pain and a high burden of psychiatric comorbidity

BACKGROUND: Chronic non-cancer pain is a common problem that is often accompanied by psychiatric comorbidity and disability. The effectiveness of a multi-disciplinary pain management program was tested in a 3 month before and after trial. METHODS: Providers in an academic general medicine clinic referred patients with chronic non-cancer pain for participation in a program that combined the skills of internists, clinical pharmacists, and a psychiatrist. Patients were either receiving opioids or being considered for opioid therapy. The intervention consisted of structured clinical assessments, monthly follow-up, pain contracts, medication titration, and psychiatric consultation. Pain, mood, and function were assessed at baseline and 3 months using the Brief Pain Inventory (BPI), the Center for Epidemiological Studies-Depression Scale scale (CESD) and the Pain Disability Index (PDI). Patients were monitored for substance misuse. RESULTS: Eighty-five patients were enrolled. Mean age was 51 years, 60% were male, 78% were Caucasian, and 93% were receiving opioids. Baseline average pain was 6.5 on an 11 point scale. The average CESD score was 24.0, and the mean PDI score was 47.0. Sixty-three patients (73%) completed 3 month follow-up. Fifteen withdrew from the program after identification of substance misuse. Among those completing 3 month follow-up, the average pain score improved to 5.5 (p = 0.003). The mean PDI score improved to 39.3 (p < 0.001). Mean CESD score was reduced to 18.0 (p < 0.001), and the proportion of depressed patients fell from 79% to 54% (p = 0.003). Substance misuse was identified in 27 patients (32%). CONCLUSIONS: A primary care disease management program improved pain, depression, and disability scores over three months in a cohort of opioid-treated patients with chronic non-cancer pain. Substance misuse and depression were common, and many patients who had substance misuse identified left the program when they were no longer prescribed opioids. Effective care of patients with chronic pain should include rigorous assessment and treatment of these comorbid disorders and intensive efforts to insure follow up

Should a Sentinel Node Biopsy Be Performed in Patients with High-Risk Breast Cancer?

A negative sentinel lymph node (SLN) biopsy spares many breast cancer patients the complications associated with lymph node irradiation or additional surgery. However, patients at high risk for nodal involvement based on clinical characteristics may remain at unacceptably high risk of axillary disease even after a negative SLN biopsy result. A Bayesian nomogram was designed to combine the probability of axillary disease prior to nodal biopsy with customized test characteristics for an SLN biopsy and provides the probability of axillary disease despite a negative SLN biopsy. Users may individualize the sensitivity of an SLN biopsy based on factors known to modify the sensitivity of the procedure. This tool may be useful in identifying patients who should have expanded upfront exploration of the axilla or comprehensive axillary irradiation

The crossroads of evidence-based medicine and health policy: implications for urology

As healthcare spending in the United States continues to rise at an unsustainable rate, recent policy decisions introduced at the national level will rely on precepts of evidence-based medicine to promote the determination, dissemination, and delivery of “best practices” or quality care while simultaneously reducing cost. We discuss the influence of evidence-based medicine on policy and, in turn, the impact of policy on the developing clinical evidence base with an eye to the potential effects of these relationships on the practice and provision of urologic care

Risk prediction models with incomplete data with application to prediction of estrogen receptor-positive breast cancer: prospective data from the Nurses' Health Study

Introduction A number of breast cancer risk prediction models have been developed to provide insight into a woman\u27s individual breast cancer risk. Although circulating levels of estradiol in postmenopausal women predict subsequent breast cancer risk, whether the addition of estradiol levels adds significantly to a model\u27s predictive power has not previously been evaluated. Methods Using linear regression, the authors developed an imputed estradiol score using measured estradiol levels (the outcome) and both case status and risk factor data (for example, body mass index) from a nested case-control study conducted within a large prospective cohort study and used multiple imputation methods to develop an overall risk model including both risk factor data from the main cohort and estradiol levels from the nested case-control study. Results The authors evaluated the addition of imputed estradiol level to the previously published Rosner and Colditz log-incidence model for breast cancer risk prediction within the larger Nurses\u27 Health Study cohort. The follow-up was from 1980 to 2000; during this time, 1,559 invasive estrogen receptor-positive breast cancer cases were confirmed. The addition of imputed estradiol levels significantly improved risk prediction; the age-specific concordance statistic increased from 0.635 ± 0.007 to 0.645 ± 0.007 (P \u3c 0.001) after the addition of imputed estradiol. Conclusion Circulating estradiol levels in postmenopausal women appear to add to other lifestyle factors in predicting a woman\u27s individual risk of breast cancer

Medical school faculty discontent: prevalence and predictors of intent to leave academic careers

<p>Abstract</p> <p>Background</p> <p>Medical school faculty are less enthusiastic about their academic careers than ever before. In this study, we measured the prevalence and determinants of intent to leave academic medicine.</p> <p>Methods</p> <p>A 75-question survey was administered to faculty at a School of Medicine. Questions addressed quality of life, faculty responsibilities, support for teaching, clinical work and scholarship, mentoring and participation in governance.</p> <p>Results</p> <p>Of 1,408 eligible faculty members, 532 (38%) participated. Among respondents, 224 (40%; CI95: 0.35, 0.44) reported that their careers were not progressing satisfactorily; 236 (42%; CI95: 0.38, 0.46) were "seriously considering leaving academic medicine in the next five years." Members of clinical departments (OR = 1.71; CI95: 1.01, 2.91) were more likely to consider leaving; members of inter-disciplinary centers were less likely (OR = 0.68; CI95: 0.47, 0.98). The predictors of "serious intent to leave" included: Difficulties balancing work and family (OR = 3.52; CI95: 2.34, 5.30); inability to comment on performance of institutional leaders (OR = 3.08; CI95: 2.07, 4.72); absence of faculty development programs (OR = 3.03; CI95: 2.00, 4.60); lack of recognition of clinical work (OR = 2.73; CI95: 1.60, 4.68) and teaching (OR = 2.47; CI95: 1.59, 3.83) in promotion evaluations; absence of "academic community" (OR = 2.67; CI95: 1.86, 3.83); and failure of chairs to evaluate academic progress regularly (OR = 2.60; CI95: 1.80, 3.74).</p> <p>Conclusion</p> <p>Faculty are a medical school's key resource, but 42 percent are seriously considering leaving. Medical schools should refocus faculty retention efforts on professional development programs, regular performance feedback, balancing career and family, tangible recognition of teaching and clinical service and meaningful faculty participation in institutional governance.</p

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features