Explore the vast range of titles available.

Platinum-based chemotherapy doublets are a standard of care for women with ovarian cancer recurring 6 months after completion of initial therapy. In this study, we aimed to explore the roles of secondary surgical cytoreduction and bevacizumab in this population, and report the results of the bevacizumab component here. The multicentre, open-label, randomised phase 3 GOG-0213 trial was done in 67 predominantly academic centres in the USA (65 centres), Japan (one centre), and South Korea (one centre). Eligible patients were adult women (aged ≥18 years) with recurrent measurable or evaluable epithelial ovarian, primary peritoneal, or fallopian tube cancer, and a clinical complete response to primary platinum-based chemotherapy, who had been disease-free for at least 6 months following last infused cycle of platinum. Patients were randomly assigned (1:1) to standard chemotherapy (six 3-weekly cycles of paclitaxel [175 mg/m2 of body surface area] and carboplatin [area under the curve 5]) or the same chemotherapy regimen plus bevacizumab (15 mg/kg of bodyweight) every 3 weeks and continued as maintenance every 3 weeks until disease progression or unacceptable toxicity. Individuals who participated in both the bevacizumab objective and surgical objective (which is ongoing) were randomly assigned (1:1:1:1) to receive either of these two chemotherapy regimens with or without prior secondary cytoreductive surgery. Randomisation for the bevacizumab objective was stratified by treatment-free interval and participation in the surgical objective. The primary endpoint was overall survival, analysed by intention to treat. This study is registered with ClinicalTrials.gov, number NCT00565851. Between Dec 10, 2007, and Aug 26, 2011, 674 women were enrolled and randomly assigned to standard chemotherapy (n=337) or chemotherapy plus bevacizumab (n=377). Median follow-up at the end of the trial on Nov 5, 2014, was 49·6 months in each treatment group (IQR 41·5–62·2 for chemotherapy plus bevacizumab; IQR 40·8–59·3 for chemotherapy), at which point 415 patients had died (214 in the chemotherapy group and 201 in the chemotherapy plus bevacizumab group). Based on pretreatment stratification data, median overall survival in the chemotherapy plus bevacizumab group was 42·2 months (95% CI 37·7–46·2) versus 37·3 months (32·6–39·7) in the chemotherapy group (hazard ratio [HR] 0·829; 95% CI 0·683–1·005; p=0·056). We identified incorrect treatment-free interval stratification data for 45 (7%) patients (equally balanced between treatment groups); a sensitivity analysis of overall survival based on the audited treatment-free interval stratification data gave an adjusted HR of 0·823 (95% CI 0·680–0·996; p=0·0447). In the safety population (all patients who initiated treatment), 317 (96%) of 325 patients in the chemotherapy plus bevacizumab group had at least one grade 3 or worse adverse event compared with 282 (86%) of 332 in the chemotherapy group; the most frequently reported of these in the chemotherapy plus bevacizumab group compared with the chemotherapy group were hypertension (39 [12%] vs two [1%]), fatigue (27 [8%] vs eight [2%]), and proteinuria (27 [8%] vs none). Two (1%) treatment-related deaths occurred in the chemotherapy group (infection [n=1] and myelodysplastic syndrome [n=1]) compared with nine (3%) in the chemotherapy plus bevacizumab group (infection [n=1], febrile neutropenia [n=1], myelodysplastic syndrome [n=1], secondary malignancy [n=1]; deaths not classified with CTCAE terms: disease progression [n=3], sudden death [n=1], and not specified [n=1]). The addition of bevacizumab to standard chemotherapy, followed by maintenance therapy until progression, improved the median overall survival in patients with platinum-sensitive recurrent ovarian cancer. Although the intention-to-treat analysis for overall survival was not significant, our sensitivity analysis based on corrected treatment-free interval stratification indicates that this strategy might be an important addition to the therapeutic armamentarium in these patients. National Cancer Institute and Genentech.

In a trial of adjuvant chemotherapy for ovarian cancer, a regimen of intravenous paclitaxel plus intraperitoneal cisplatin and paclitaxel was superior to intravenous paclitaxel plus intravenous cisplatin. In a trial of adjuvant chemotherapy for ovarian cancer, a regimen of intravenous paclitaxel plus intraperitoneal cisplatin and paclitaxel was superior to intravenous paclitaxel plus intravenous cisplatin. Ovarian cancer is the leading cause of death from a gynecologic cancer in the United States. 1 In most cases, the high death rate is due to tumor that has spread beyond the ovary at the time of diagnosis. 2 In the United States, the standard chemotherapy for the initial treatment of ovarian cancer is a combination of a platinum analogue with paclitaxel. 3 , 4 With modern surgical interventions and contemporary chemotherapy, most patients attain complete clinical remission. 3 , 5 The majority of them, however, will eventually have a relapse and die of the disease. The peritoneal cavity is the principal site of disease . . .

Cervical intraepithelial neoplasia grade 3 (CIN3), the immediate cervical cancer precursor, is a target of cervical cancer prevention. However, less than half of CIN3s will progress to cancer. Routine treatment of all CIN3s and the majority of CIN2s may lead to overtreatment of many lesions that would not progress. To improve our understanding of CIN3 natural history, we performed a detailed characterization of CIN3 heterogeneity in a large referral population in the US. We examined 309 CIN3 cases in the SUCCEED, a large population-based study of women with abnormal cervical cancer screening results. Histology information for 12 individual loop electrosurgical excision procedure (LEEP) segments was evaluated for each woman. We performed case-case comparisons of CIN3s to analyze determinants of heterogeneity and screening test performance. CIN3 cases varied substantially by size (1-10 LEEP segments) and by presentation with concomitant CIN2 and CIN1. All grades of CINs were equally distributed over the cervical surface. In half of the women, CIN3 lesions were found as multiple distinct lesions on the cervix. Women with large and solitary CIN3 lesions were more likely to be older, have longer sexual activity span, and have fewer multiple high risk HPV infections. Screening frequency, but not HPV16 positivity, was an important predictor of CIN3 size. Large CIN3 lesions were also characterized by high-grade clinical test results. We demonstrate substantial heterogeneity in clinical and pathological presentation of CIN3 in a US population. Time since sexual debut and participation in screening were predictors of CIN3 size. We did not observe a preferential site of CIN3 on the cervical surface that could serve as a target for cervical biopsy. Cervical cancer screening procedures were more likely to detect larger CIN3s, suggesting that CIN3s detected by multiple independent diagnostic tests may represent cases with increased risk of invasion.

Values lie at the heart of an individual’s belief system, serving as prototypes from which attitudes and behaviors are subsequently manufactured. Attitudes and behaviors may evolve over time, but values represent a set of more enduring beliefs. This study examines the influence of values on travel mode choice behavior. It is argued that personal values influence individual attitudes towards different alternative attributes, which in turn impact modal choices. Using data from a sample of 519 German commuters drawn from a consumer panel, the study estimates an integrated choice and latent variable model of travel mode choice that allows for hierarchical relationships between the latent variables and flexible substitution patterns across the modal alternatives. Results from the empirical application support the value-attitude-behavior hierarchical model of cognition, and provide insights to planners and policy-makers on how better to sell public transit as a means of travel.

Background Identification of human papillomavirus (HPV) DNA in cervical tissue is important for understanding cervical carcinogenesis and for evaluating cervical cancer prevention approaches. However, HPV genotyping using formalin-fixed, paraffin-embedded (FFPE) tissues is technically challenging. We evaluated the performance of four commonly used genotyping methods on FFPE cervical specimens conducted in different laboratories and compared to genotyping results from cytological samples. Methods We included 60 pairs of exfoliated-cell and FFPE specimens from women with histologically confirmed cervical intraepithelial lesions grade 2 or 3. Cytology specimens were genotyped using the Linear Array assay. Four expert laboratories processed tissue specimens using different preparation methods and then genotyped the resultant sample preparations using four different HPV genotyping methods: SPF 10 -PCR DEIA LiPA 25 (version 1), Inno-LiPA, Linear Array and the Onclarity assay. Percentage agreement, kappa statistics and McNemar’s chi-square were calculated for each comparison of different methods and specimen types. Results Overall agreement with respect to carcinogenic HPV status for FFPE samples between different methods was: 81.7, 86.7 and 91.7 % for Onclarity versus Inno-LiPA, Linear Array and SPF-LiPA 25 , respectively; 81.7 and 85.0 % for Linear Array versus Inno-LiPA and SPF-LiPA 25 , respectively; and 86.7 % for SPF-LiPA 25 versus Inno-LiPA. Type-specific agreement was >88.3 % for all pair-wise comparisons. Comparisons with cytology specimens resulted in overall agreements from 80 to 95 % depending on the method and type-specific agreement was >90 % for most comparisons. Conclusions Our data demonstrate that the four genotyping methods run by expert laboratories reliably detect HPV DNA in FFPE specimens with some variation in genotype-specific detection.

Ecological restoration is frequently guided by reference conditions describing a successfully restored ecosystem; however, the causes and magnitude of ecosystem degradation vary, making simple knowledge of reference conditions insufficient for prioritizing and guiding restoration. Ecological reference models provide further guidance by quantifying reference conditions, as well as conditions at degraded states that deviate from reference conditions. Many reference models remain qualitative, however, limiting their utility. We quantified and evaluated a reference model for southeastern U.S. longleaf pine woodland understory plant communities. We used regression trees to classify 232 longleaf pine woodland sites at three locations along the Atlantic coastal plain based on relationships between understory plant community composition, soils (which broadly structure these communities), and factors associated with understory degradation, including fire frequency, agricultural history, and tree basal area. To understand the spatial generality of this model, we classified all sites together and for each of three study locations separately. Both the regional and location-specific models produced quantifiable degradation gradients-i.e., progressive deviation from conditions at 38 reference sites, based on understory species composition, diversity and total cover, litter depth, and other attributes. Regionally, fire suppression was the most important degrading factor, followed by agricultural history, but at individual locations, agricultural history or tree basal area was most important. At one location, the influence of a degrading factor depended on soil attributes. We suggest that our regional model can help prioritize longleaf pine woodland restoration across our study region; however, due to substantial landscape-to-landscape variation, local management decisions should take into account additional factors (e.g., soil attributes). Our study demonstrates the utility of quantifying degraded states and provides a series of hypotheses for future experimental restoration work. More broadly, our work provides a framework for developing and evaluating reference models that incorporate multiple, interactive anthropogenic drivers of ecosystem degradation.

For evacuations, people must make the critical decision to evacuate or stay followed by a multi-dimensional choice composed of concurrent decisions of their departure time, transportation mode, route, destination, and shelter type. These choices have important impacts on transportation response and evacuation outcomes. While extensive research has been conducted on hurricane evacuation behavior, little is known about wildfire evacuation behavior. To address this critical research gap, particularly related to joint choice-making in wildfires, we surveyed individuals impacted by the 2017 December Southern California Wildfires (n = 226) and the 2018 Carr Wildfire (n = 284). Using these data, we contribute to the literature in two key ways. First, we develop two latent class choice models (LCCMs) to evaluate the factors that influence the decision to evacuate or stay/defend. We find an evacuation keen class and an evacuation reluctant class that are influenced differently by mandatory evacuation orders. This nuance is further supported by different membership of people to the classes based on demographics and risk perceptions. Second, we develop two portfolio choice models (PCMs), which jointly model choice dimensions to assess multi-dimensional evacuation choice. We find several similarities between wildfires including a joint preference for within-county and nighttime evacuations and a joint dislike for within-county and highway evacuations. Altogether, this paper provides evidence of heterogeneity in response to mandatory evacuation orders for wildfires, distinct membership of populations to different classes of people for evacuating or staying/defending, and clear correlation among key wildfire evacuation choices that necessitates joint modeling to holistically understanding wildfire evacuation behavior.

An NCI-sponsored, randomized clinical trial tested whether patients with relapsed ovarian cancer might benefit from surgical debulking of tumors followed by chemotherapy, as compared with chemotherapy alone. No significant outcome differences were noted between the patients who underwent surgery and those treated with chemotherapy alone.

Automated driving technologies are currently penetrating the market, and the coming fully autonomous cars will have far-reaching, yet largely unknown, implications. A critical unknown is the impact on traveler behavior, which in turn impacts sustainability, the economy, and wellbeing. Most behavioral studies, to date, either focus on safety and human factors (driving simulators; test beds), assume travel behavior implications (microsimulators; network analysis), or ask about hypothetical scenarios that are unfamiliar to the subjects (stated preference studies). Here we present a different approach, which is to use a naturalistic experiment to project people into a world of self-driving cars. We mimic potential life with a privately-owned self-driving vehicle by providing 60 h of free chauffeur service for each participating household for use within a 7-day period. We seek to understand the changes in travel behavior as the subjects adjust their travel and activities during the chauffeur week when, as in a self-driving vehicle, they are explicitly relieved of the driving task. In this first pilot application, our sample consisted of 13 subjects from the San Francisco Bay area, drawn from three cohorts: millennials, families, and retirees. We tracked each subject’s travel for 3 weeks (the chauffeur week, 1 week before and 1 week after) and conducted surveys and interviews. During the chauffeur week, we observed sizable increases in vehicle-miles traveled and number of trips, with a more pronounced increase in trips made in the evening and for longer distances and a substantial proportion of “zero-occupancy” vehicle-miles traveled.

Although the basic structure of logit-mixture models is well understood, important identification and normalization issues often get overlooked. This paper addresses issues related to the identification of parameters in logit-mixture models containing normally distributed error components associated with alternatives or nests of alternatives (normal error component logit mixture, or NECLM, models). NECLM models include special cases such as unrestricted, fixed covariance matrices; alternative-specific variances; nesting and cross-nesting structures; and some applications to panel data. A general framework is presented for determining which parameters are identified as well as what normalization to impose when specifying NECLM models. It is generally necessary to specify and estimate NECLM models at the levels, or structural, form. This precludes working with utility differences, which would otherwise greatly simplify the identification and normalization process. Our results show that identification is not always intuitive; for example, normalization issues present in logit-mixture models are not present in analogous probit models. To identify and properly normalize the NECLM, we introduce the 'equality condition', an addition to the standard order and rank conditions. The identifying conditions are worked through for a number of special cases, and our findings are demonstrated with empirical examples using both synthetic and real data.