Approaches to decision-making among late-stage melanoma patients: a multifactorial investigation.

PurposeThe treatment decisions of melanoma patients are poorly understood. Most research on cancer patient decision-making focuses on limited components of specific treatment decisions. This study aimed to holistically characterize late-stage melanoma patients' approaches to treatment decision-making in order to advance understanding of patient influences and supports.Methods(1) Exploratory analysis of longitudinal qualitative data to identify themes that characterize patient decision-making. (2) Pattern analysis of decision-making themes using an innovative method for visualizing qualitative data: a hierarchically-clustered heatmap. Participants were 13 advanced melanoma patients at a large academic medical center.ResultsExploratory analysis revealed eight themes. Heatmap analysis indicated two broad types of patient decision-makers. "Reliant outsiders" relied on providers for medical information, demonstrated low involvement in decision-making, showed a low or later-in-care interest in clinical trials, and expressed altruistic motives. "Active insiders" accessed substantial medical information and expertise in their networks, consulted with other doctors, showed early and substantial interest in trials, demonstrated high involvement in decision-making, and employed multiple decision-making strategies.ConclusionWe identified and characterized two distinct approaches to decision-making among patients with late-stage melanoma. These differences spanned a wide range of factors (e.g., behaviors, resources, motivations). Enhanced understanding of patients as decision-makers and the factors that shape their decision-making may help providers to better support patient understanding, improve patient-provider communication, and support shared decision-making

Dynamic Matrix Factorization with Priors on Unknown Values

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.Comment: in the Proceedings of 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

Beyond Exploratory: A Tailored Framework for Assessing Rigor in Qualitative Health Services Research

Objective: To propose a framework for assessing the rigor of qualitative research that identifies and distinguishes between the diverse objectives of qualitative studies currently used in patient-centered outcomes and health services research (PCOR and HSR). Study Design: Narrative review of published literature discussing qualitative guidelines and standards in peer-reviewed journals and national funding organizations that support PCOR and HSR. Principal Findings: We identify and distinguish three objectives of current qualitative studies in PCOR and HSR: exploratory, descriptive, and comparative. For each objective, we propose methodological standards that can be used to assess and improve rigor across all study phases—from design to reporting. Similar to quantitative studies, we argue that standards for qualitative rigor differ, appropriately, for studies with different objectives and should be evaluated as such. Conclusions: Distinguishing between different objectives of qualitative HSR improves the ability to appreciate variation in qualitative studies as well as appropriately evaluate the rigor and success of studies in meeting their own objectives. Researchers, funders, and journal editors should consider how adopting the criteria for assessing qualitative rigor outlined here may advance the rigor and potential impact of qualitative research in patient-centered outcomes and health services research

The K2 Galactic Caps Project - going beyond the Kepler field and ageing the Galactic disc

Analyses of data from spectroscopic and astrometric surveys have led to conflicting results concerning the vertical characteristics of the Milky Way. Ages are often used to provide clarity, but typical uncertainties of >40 per cent from photometry restrict the validity of the inferences made. Using the Kepler APOKASC sample for context, we explore the global population trends of two K2 campaign fields (3 and 6), which extend further vertically out of the Galactic plane than APOKASC. We analyse the properties of red giant stars utilizing three asteroseismic data analysis methods to cross-check and validate detections. The Bayesian inference tool PARAM is used to determine the stellar masses, radii, and ages. Evidence of a pronounced red giant branch bump and an [α/Fe] dependence on the position of the red clump is observed from the K2 fields radius distribution. Two peaks in the age distribution centred at ∼5 and ∼12 Gyr are found using a sample with σage 0.1) stars. As a function of vertical distance from the Galactic mid-plane (|Z|), the age distribution shows a transition from a young to old stellar population with increasing |Z| for the K2 fields. Further coverage of campaign targets with high-resolution spectroscopy is required to increase the yield of precise ages achievable with asteroseismology.We gratefully acknowledge the support of the UK Science and Technology Facilities Council (STFC). BMR, AM, GRD, BM, LG, and SK are grateful to the International Space Science Institute (ISSI) for support provided to the asteroSTEP ISSI International Team. AM acknowledges support from the ERC Consolidator Grant funding scheme (project ASTEROCHRONOMETRY, G.A. no. 772293). CC acknowledges support from DFG Grant CH1188/2- 1 and from the ChETEC COST Action (CA16117), supported by COST (European Cooperation in Science and Technology). BMR would like to thank the AIP for temporarily hosting him during the studies for this work. LC is the recipient of the ARC Future Fellowship FT160100402. SM acknowledges support from NASA grants NNX16AJ17G and NNX15AF13G, by the National Science Foundation grant AST-1411685 and the Ramon y Cajal fellowship number RYC-2015–17697. RAG acknowledges the funding received from the CNES through the PLATO grant. PJ acknowledges FONDECYT Iniciacion Grant Number 11170174. ´ TSR acknowledges financial support from Premiale 2015 MITiC (PI B. Garilli). AG acknowledges support from theSwedish National Space Board. Funding for the Stellar Astrophysics Centre is provided by the Danish National Research Foundation (Grant DNRF106). Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS acknowledges support and resources from the Center for HighPerformance Computing at the University of Utah. The SDSS web site is www.sdss.org

Chronologically dating the early assembly of the Milky Way

The standard cosmological model predicts that galaxies are built through hierarchical assembly on cosmological timescales1,2. The Milky Way, like other disk galaxies, underwent violent mergers and accretion of small satellite galaxies in its early history. Owing to Gaia Data Release 23 and spectroscopic surveys4, the stellar remnants of such mergers have been identified5–7. The chronological dating of such events is crucial to uncover the formation and evolution of the Galaxy at high redshift, but it has so far been challenging due to difficulties in obtaining precise ages for these oldest stars. Here we combine asteroseismology—the study of stellar oscillations—with kinematics and chemical abundances to estimate precise stellar ages (~11%) for a sample of stars observed by the Kepler space mission8. Crucially, this sample includes not only some of the oldest stars that were formed inside the Galaxy but also stars formed externally and subsequently accreted onto the Milky Way. Leveraging this resolution in age, we provide compelling evidence in favour of models in which the Galaxy had already formed a substantial population of its stars (which now reside mainly in its thick disk) before the infall of the satellite galaxy Gaia-Enceladus/Sausage5,6 around 10 billion years ago

New light on the Gaia DR2 parallax zero-point: Influence of the asteroseismic approach, in and beyond the Kepler field

The importance of studying the Gaia DR2 parallax zero-point by external means was underlined by the articles that accompanied the release, and initiated by several works making use of Cepheids, eclipsing binaries, and asteroseismology. Despite a very efficient elimination of basic-angle variations, a small fluctuation remains and shows up as a small offset in the Gaia DR2 parallaxes. By combining astrometric, asteroseismic, spectroscopic, and photometric constraints, we undertake a new analysis of the Gaia parallax offset for nearly 3000 red-giant branch (RGB) and 2200 red clump (RC) stars observed by Kepler, as well as about 500 and 700 red giants (all either in the RGB or RC phase) selected by the K2 Galactic Archaeology Program in campaigns 3 and 6. Engaging in a thorough comparison of the astrometric and asteroseismic parallaxes, we are able to highlight the influence of the asteroseismic method, and measure parallax offsets in the Kepler field that are compatible with independent estimates from literature and open clusters. Moreover, adding the K2 fields to our investigation allows us to retrieve a clear illustration of the positional dependence of the zero-point, in general agreement with the information provided by quasars. Lastly, we initiate a two-step methodology to make progress in the simultaneous calibration of the asteroseismic scaling relations and of the Gaia DR2 parallax offset, which will greatly benefit from the gain in precision with the third data release of Gaia.This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. SK, AM, BM, GRD, BMR, DB, and LG are grateful to the International Space Science Institute (ISSI) for support provided to the asteroSTEP ISSI International Team. AM, WJC, GRD, BMR, YPE, and TSHN acknowledge the support of the UK Science and Technology Facilities Council (STFC). AM acknowledges support from the ERC Consolidator Grant funding scheme (project ASTEROCHRONOMETRY, G.A. n. 772293). LC is the recipient of the ARC Future Fellowship FT160100402. TSR acknowledges financial support from Premiale 2015 MITiC (PI B. Garilli). This work was supported by FCT/MCTES through national funds and by FEDER – Fundo Europeu de Desenvolvimento Regional through COMPETE2020 – Programa Operacional Competitividade e Internacionalização by grants: UID/FIS/04434/2019; PTDC/FIS-AST/30389/2017 & POCI-01-0145- FEDER-030389. DB is supported in the form of a work contract funded by national funds through Fundação para a Ciência e Tecnologia (FCT)

AIMS - A new tool for stellar parameter determinations using asteroseismic constraints

A key aspect in the determination of stellar properties is the comparison of observational constraints with predictions from stellar models. Asteroseismic Inference on a Massive Scale (AIMS) is an open source code that uses Bayesian statistics and a Markov Chain Monte Carlo approach to find a representative set of models that reproduce a given set of classical and asteroseismic constraints. These models are obtained by interpolation on a pre-calculated grid, thereby increasing computational efficiency. We test the accuracy of the different operational modes within AIMS for grids of stellar models computed with the Li\`ege stellar evolution code (main sequence and red giants) and compare the results to those from another asteroseismic analysis pipeline, PARAM. Moreover, using artificial inputs generated from models within the grid (assuming the models to be correct), we focus on the impact on the precision of the code when considering different combinations of observational constraints (individual mode frequencies, period spacings, parallaxes, photospheric constraints,...). Our tests show the absolute limitations of precision on parameter inferences using synthetic data with AIMS, and the consistency of the code with expected parameter uncertainty distributions. Interpolation testing highlights the significance of the underlying physics to the analysis performance of AIMS and provides caution as to the upper limits in parameter step size. All tests demonstrate the flexibility and capability of AIMS as an analysis tool and its potential to perform accurate ensemble analysis with current and future asteroseismic data yields.Comment: Accepted for publication in MNRAS. 17 pages, 17 figure

Discovering temporal regularities in retail customers’ shopping behavior

In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customerâ\u80\u99s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity