A novel mobile phone and tablet application for automatized calculation of pain extent.

Background: Pain drawings (PDs) are used for assessing pain extent as a complementary outcome to other pain measurements, consisting of shading a body chart template to report the location and extent of pain. However, the accuracy and reliability of digital PDs remain controversial due to the heterogeneity of methods used. This study aimed to develop an easy-to-use application for assessing its diagnostic accuracy in comparison with the classic paper-and-pencil method. Methods: A test-retest reliability study was conducted, recruiting 95 patients with musculoskeletal pain symptoms. Participants shaded 2 sets of 3 different PDs (paper-and-pencil PD, digital PD using the finger and digital PD using the digital stylus). Intraclass correlation coefficients (ICC), standard error of measurement and minimal detectable changes (MDC) were calculated for each method. Finally, repeated measure analysis of variance assessed the mean differences between trials and methods and the convergent validity between methods was calculated using Pearson’s correlation coefficients. Results: All methods were excellently reliable (all, ICC>0.94). However, digital PDs obtained higher ICCs (ICC≥0.970) and greater accuracy to detect whether changes reflect a real change and are not due to a measurement errors (MDC = 0.72%– 0.80 % for digital PDs versus MDC = 1.13 % for paper-and-Pencil PDs). No significant score differences were found among the instruments for assessing pain extent (p > 0.05). Finally, the PAIN EXTENT app showed adequate convergent validity (r > 0.850). Conclusion: The PAIN EXTENT app is a fast and easy-to-use instrument compatible with operative systems and devices commonly used for assessing and monitoring pain extent in the clinical and research settings.post-print2,22 M

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data