Access to Print in Low‐Income and Middle‐Income Communities: An Ecological Study of Four Neighborhoods

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/88026/1/RRQ.36.1.1.pd

Adalimumab medium-term dosing strategy in moderate-to-severe hidradenitis suppurativa: integrated results from the phase III randomized placebo-controlled PIONEER trials

Background: Weekly adalimumab (Humira®) is approved for the treatment of hidradenitis suppurativa (HS) based on the 12-week placebo-controlled periods of the two phase III PIONEER trials. Objectives: Using PIONEER integrated trial results, we aimed to evaluate the optimal medium-term adalimumab maintenance dosing strategy for moderate-to-severe HS. Methods: Each trial had two double-blind periods; 12-week Period A and 24-week Period B. Patients randomized to adalimumab 40 mg every week (ADAew) (Period A), were rerandomized in Period B to ADAew (ADAew/ew), ADA every other week (ADAew/eow), or placebo (ADAew/pbo). Placebo-randomized patients were reassigned in Period B to ADAew (PIONEER I) or placebo (PIONEER II). The primary outcome was HS Clinical Response (HiSCR). Patients who lost response during Period B were discontinued from the study and offered an option to enter the open-label extension (OLE) to receive ADAew. Results are reported across the two study periods, and data were combined from the two study periods and the OLE. Results: For week-12 HiSCR achievers, the HiSCR week-36 rate was 48·1% (ADAew/ew) vs. 46·2% (ADAew/eow) and 32·1% (ADAew/pbo). Combining (post hoc) these patients with week-12 partial responders further differentiated outcomes in Period B (ADAew/ew 55·7% vs. ADAew/eow 40·0% and ADAew/pbo 30·1%). Period-B adverse-event rates were ADAew/ew 59·6% vs. ADAew/eow 57·4% and ADAew/pbo 65·0%. One patient (ADAew/ew) reported a serious infection. Conclusions: Weekly adalimumab treatment, effective throughout 36 weeks, was the optimal maintenance medium-term dosing regimen for this population. At least partial response after 12 weeks with continued weekly dosing had better outcomes than dose reduction or interruption. Patients who do not show at least a partial response to weekly adalimumab by week 12 are unlikely to benefit from continued therapy. No new safety risks were identified. What's already known about this topic?. Hidradenitis suppurativa (HS) is a chronic inflammatory disease, commonly misinterpreted as an infection and treated with long-term antibiotic regimens or surgical incisions. Based on the chronicity of HS and the lack of evidence for efficacious and safe long-term HS treatments, it is important to evaluate medium- to long-term therapies for HS. Weekly adalimumab (Humira®) is approved for the treatment of moderate-to-severe HS based on the two phase III PIONEER trials. What does this study add?. This study pooled data from the two PIONEER trials, providing a more robust assessment of outcomes. After at least partial treatment success with weekly adalimumab short-term therapy (12 weeks), continuing weekly dosing during the subsequent 24 weeks had better outcomes than dose reduction or treatment interruption. Patients who do not show at least a partial response to weekly adalimumab by week 12 are unlikely to benefit from continued therapy

An Empirical Study of Category Skew on Feature Selection for Text Categorization

Abstract. In this paper, we present an empirical comparison of the effects of category skew on six feature selection methods. The methods were evaluated on 36 datasets generated from the 20 Newsgroups, OHSUMED, and Reuters-21578 text corpora. The datasets were generated to possess particular category skew characteristics (i.e., the number of documents assigned to each category). Our objective was to determine the best performance of the six feature selection methods, as measured by F-measure and Precision, regardless of the number of features needed to produce the best performance. We found the highest F-measure values were obtained by bi-normal separation and information gain and the highest Precision values were obtained by categorical proportional difference and chi-squared.

Classification of Small Datasets: Why Using Class-Based Weighting Measures?

International audienceIn text classification, providing an efficient classifier even if the number of documents involved in the learning step is small remains an important issue. In this paper we evaluate the performance of traditional classification methods to better evaluate their limitation in the learning phase when dealing with small amount of documents. We thus propose a new way for weighting features which are used for classifying. These features have been integrated in two well known classifiers: Class-Feature-Centroid and Naïve Bayes, and evaluations have been performed on two real datasets. We have also investigated the influence on parameters such as number of classes, documents or words in the classification. Experiments have shown the efficiency of our proposal relatively to state of the art classification methods. Either with a very few amount of data or with a small number of features that can be extracted from poor content documents, we show that our approach performs well

Techniques for Improving the Performance of Naive Bayes for Text Classification

Naive Bayes is often used in text classification applications and experiments because of its simplicity and effectiveness. However, its performance is often degraded because it does not model text well, and by inappropriate feature selection and the lack of reliable confidence scores. We address these problems and show that they can be solved by some simple corrections. We demonstrate that our simple modifications are able to improve the performance of Naive Bayes for text classification significantly