Cancer of Unknown Primary Site:A Review of 28 Cases and the Efficacy of Cisplatin/Docetaxel Therapy at a Single Institute in Japan

We evaluated the efficacy and toxicity of cisplatin/docetaxel (CDDP/TXT) chemotherapy and identified prognostic factors in Japanese patients with cancer of unknown primary site (CUP). Twenty-eight consecutive patients seen at a single institute were reviewed retrospectively. Sixteen patients were treated with TXT 80mg/m2, followed by CDDP 75mg/m2. The overall response rate to CDDP/TXT treatment was 62.5%, with a median survival time (MST) of 22.7 months. Common adverse reactions were myelosuppression and hyponatremia. The MST of all 28 patients with CUP was 8.3 months, and the 1-year overall survival rate was 45.6%. Univariate analysis identified 5 prognostic factors:performance status, liver involvement, bone involvement, pleural involvement, and lymph node involvement. In conclusion, CDDP/TXT chemotherapy is effective with tolerable toxicity in patients with CUP. Japanese patients with CUP might be chemosensitive and may survive longer

Review of Data Sources for School to Work Transitions by Youth with Disabilities

DE15_PDF1.pdf: 1031 downloads, before Oct. 1, 2020.0-DE15_TXT1.txt: 200 downloads, before Oct. 1, 2020

Adaptive Estimation of Autoregressive Models with Time-Varying Variances

Stable autoregressive models of known finite order are considered with martingale differences errors scaled by an unknown nonparametric time-varying function generating heterogeneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution or combinations of the two. This paper develops kernel-based estimators of the residual variances and associated adaptive least squares (ALS) estimators of the autoregressive coefficients. These are shown to be asymptotically efficient, having the same limit distribution as the infeasible generalized least squares (GLS). Comparisons of the efficient procedure and the ordinary least squares (OLS) reveal that least squares can be extremely inefficient in some cases while nearly optimal in others. Simulations show that, when least squares work well, the adaptive estimators perform comparably well, whereas when least squares work poorly, major efficiency gains are achieved by the new estimators.Adaptive estimation, Autoregression, Heterogeneity, Weighted regression

Adaptive Estimation of Autoregressive Models with Time-Varying Variances

Stable autoregressive models of known finite order are considered with martingale differences errors scaled by an unknown nonparametric time-varying function generating heterogeneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution or combinations of the two. This paper develops kernel-based estimators of the residual variances and associated adaptive least squares (ALS) estimators of the autoregressive coefficients. These are shown to be asymptotically efficient, having the same limit distribution as the infeasible generalized least squares (GLS). Comparisons of the efficient procedure and ordinary least squares (OLS) reveal that least squares can be extremely inefficient in some cases while nearly optimal in others. Simulations show that, when least squares work well, the adaptive estimators perform comparably well, whereas when least squares work poorly, major efficiency gains are achieved by the new estimators.Adaptive estimation, Autoregression, Heterogeneity, Weighted regression

Long horizon regressions with moderate deviations from a unit root

We consider long horizon regressions where the predictor with unknown degree of persistence follows a process of moderate deviations from a unit root. Some asymptotic properties of OLS estimator and of the t statistic are presented.

Thinking About Fairness & Achieving Balance in Mediation

This Article identifies five sources of bias present in mediation practice: (1) categorization, (2) attribution, (3) metaphorical expression, (4) norming, and (5) framing. For each of these cognitive efficiencies, which contribute to bias in mediation, the author provides practice recommendations. Finally, the author suggests that the Article be read as a proposal for further thought and inquiry to improve the fairness of mediators

The Evolution of Asbestos Bankruptcy Trust Distribution Plans

The evolution of asbestos litigation from the early 1970s to the present has become the source of much analysis. One commentator divides this history into several phases: the heroic phase, bureaucratic floundering, adaptation and maturity, search for global settlement, expansion of the number of cases, and legislative reform in a new era. A neglected aspect of the history of asbestos litigation has been the evolution of asbestos bankruptcy trust distribution plans. Since 1982 there have been more than 70 corporations which have filed bankruptcy proceedings because of their exposure to asbestos liability. As these corporations emerge from bankruptcy, their plans of reorganization establish trust distribution plans to pay asbestos claimants. These distribution plans provide a unique window into the evolution of a marketplace for the evaluation of asbestos claims among plaintiffs’ lawyers. Notwithstanding the “maturity”5 of the mass tort, this evolution reveals the historic development of relative values and differentiation among asbestos personal injury claims from the perspective of lawyers who represent plaintiffs. Specifically, plaintiffs’ counsel have voluntarily strengthened qualification criteria and altered the balance of payments for discrete diseases to deal with the scarcity of resources in the bankruptcy trust context