dataesr/scientific-paragraphs-categorization huggingface dataset:
Error ¼ 100 Ã ðSurf À Surf Truth Þ=Surf Truth ð1Þ Where Surf is the surface assessment measured by the software and SurfTruth is the known phantom surface. Precision. We evaluated the precision of surface assessments relying on abdominal thoracic test-retest data. We used 11 test-retest cases from the RIDER database  [12]. From the original dataset of 32 patients, we rejected scans acquired with a field of view smaller than body circumference. We performed a Bland Altman analysis to evaluate the precision of surface measurement  [13]. Using each couple of test-retest acquisitions, we computed the proportional difference of measurements to assess bias, Standard deviation (SD) and limit of agreement (LoA). Compliance of segmentations. We checked the correctness of segmentation and, indirectly, confirmed the integrity of measurements, since it is possible to obtain believable measurements with corrupted segmentations.
Inter-method variability In addition to the software qualification, we were interested in comparing the most used anthropomorphic models of BSA assessments (Du Bois & Du Bois  [5] , Mosteller  [14] , Haycock  [15] , Boyd  [16]  and Gehan & George  [17] ) to iBSA by analyzing the inter-method variability significance. We tested 26 patients from the whole body CT scan dataset who had height and weight properly documented. To assess inter-method variability performance, we applied a Bland Altman analysis between anthropomorphic models of BSA, notably the regular Du Bois & Du Bois, and iBSA according to Eq 2: BSA ¼ 0:20247 Ã H 0:725 Ã W 0:425 ð2Þ Bias, SD and LoA were drawn  [13]  and compared. We compared inter-method variances and bias using respectively F-test and t-test.
Finally, several of these formulas have limitations when applied to specific groups, such as sarcopenic or obese patients, using equations that were not designed for these populations or that were derived from significantly under-represented specific populations  [5] [6]. By design, iBSA is not prone to these mentioned limitations, iBSA's reliability is good and it can be routinely available because evaluations are fully automatic. We believe that these features are essential for integration in clinical workflows and use in transversal or longitudinal evaluations. We recognize a few limitations in computing and analyzing iBSA biomarker. Our study is based on a dataset of whole body-CT scans that we used to purposely assess iBSA, however patients generally undergo partial-body CT scans, such as thoracic, abdominal, or thoracic-abdominal scans  [6].
Thus, extracting the iBSA biomarker for clinical routine would require extrapolating partial measurements to the whole body  [20]. Another limitation would arise from the axial field of view for the scanner that may preclude the use of this method in very obese patients whose body contour at least partially exceeds the camera field of view. Also, the body contact areas, such as the regions between the legs, the arms, and the hands ( Even if an assessment bias is generally not regarded as a major limitation, since it can be systematically subtracted from the assessment, we found a software accuracy as low as 1. 5%. The test-retest data analysis reported a low random error. The good precision of our measurements should be considered as a worst case situation since our dataset featured a narrow field of view.
Therefore, the measured surface was small and the percentage of relative error maximized. The evaluation of accuracy was obtained using a single non-anthropomorphic phantom because no commercial anthropomorphic phantom is provided with a corresponding BSA feature. We decided to rely on a geometric phantom designed for scanner maintenance and imaged multiple times in all directions. The test-retest dataset was limited (11 patients) for two reasons: 1) these were uncommon data and 2) part of the RIDER data had truncated field of view and were therefore not usable. Furthermore, not all parts of the body were represented in this dataset, only thoracic or thoracic-abdominal scans were included. Our whole-body dataset was reconstructed using 5mm slices unlike images used in the two other validation steps where reconstructions were with 1.
To achieve these goals, the UMBRELLA SIOP-RTSG 2016 protocol (the UMBRELLA protocol), approved by the SIOP-RTSG and by the ethical committee in the country of the sponsor (Germany) (EudraCT number 2016-004180-39), is currently being implemented in over 50 countries  22, 23. The management of paediatric CCSK is addressed in the UMBRELLA protocol. This Consensus Statement describes the rationale for the diagnosis, treatment, and follow-up recommendations for children with CCSK included in the UMBRELLA protocol. Importantly, owing to the rarity of CCSK, no randomized trials specifically investigating CCSK have been performed, which limits the level of evidence available. Consequently, recommendations for CCSK included in the UMBRELLA protocol are based on synthesis of collated evidence (including observational studies and randomized trials not specific to CCSK) by experts in the field of CCSK to select the current best-available treatment.
Acknowledgements The  International Society of Paediatric Oncology-Renal Tumour Study Group  ( SIOP-RTSG ) acknowledges the outstanding contribution of  I. Leuschner , a SIOP pathology panel member and co-chair who was dedicated to improving the outcomes of children with cancer. In this respect, he served as a reference pathologist for many countries. His sudden death is a great loss for us and our society. The authors will miss him as a friend and colleague and will keep him in their lasting memories. This work was supported by the  Paediatric Oncology Centre Society for Research (KOCR) , the  DaDa foundation , the  Gesellschaft für Pädiatrische Onkologie und Hämatologie, and Deutsche Krebshilfe  (grant  50-2709-Gr2 ). K. P. -J. 's contribution is supported by the  UK National Institute of Health Research Biomedical Research Centre at Great Ormond Street Hospital , the  Great Ormond Street Hospital Children's Charity , and  Cancer Research UK  (grant no.
The updated spectroscopy includes analyses of spectra recorded at temperatures from 170 to 296 K, representing a significant advance. Parameters for broadening of CO 2 by H 2 O are from  Sung et al. (2009)  for the 4. 3 µm CO 2 band and extrapolated to OCO-2's CO 2 bands. Validation of the ABSCO v5. 0 tables using up-looking TCCON spectra is described in  Drouin et al. (2017)  and  Oyafuso et al. (2017). We note one important difference between the reference databases and our CO 2 absorption coefficients. We found it necessary to incorporate additional absorption in the center of the 2. 06 µm band. This additional absorption was parameterized in order to reduce errors in retrievals with TCCON up-looking spectra. Further details can be found in  Thompson et al. (2012)  and  Oyafuso et al. (2017).
High-latitude validation with TCCON remains challenging, where OCO-2 data are still sparse. Table  7  lists the TCCON sites used as truth proxies in this work. Training data ranges correspond to the quality-filtering and bias-correction procedures described in Sect. 4. 2 and 4. 3, respectively. Validation data ranges correspond to the basic validation described in Sect. 5. Our colocation requirements for B8 were similar to those used for  Wunch et al. (2017) , in which we required that OCO-2 footprints were within 2. 5 • latitude and 5. 0 • longitude of the TCCON station, and that the observations occurred within 2 h of each other. These requirements were modified slightly for the Caltech, Armstrong, and Tsukuba stations in order to discriminate satellite observations taken over the nearby megacities of Los Angeles and Tokyo.
Because of additional station data and a longer training period, there were roughly twice as many station months of valid colocations for B8 as compared to our B7 training (roughly 400 versus 190 station months). We estimate TCCON colocation errors to be on the order of 0. 5 ppm, due to both colocation errors and TCCON station-level biases  (Hedelius et al. , 2017). Even with these small errors, TCCON is an incomplete validation source due to its limited spatial coverage. For example, there are few stations in the tropics, none in the central Pacific or central Asia, and, with the exception of Armstrong, none in bright desert regions. Except when specifically stated, we employed the OCO-2 averaging kernel correction. A general treatment of averaging kernel corrections was first given in  Wunch et al.
Model-based truth proxies The third validation data set is based on results from global carbon flux inverse models, and is referred to as the "multimodel median". In order to evaluate OCO-2 retrievals against a posteriori results from an array of models, and to avoid the biases in one particular model, a suite of six models sampled at the OCO-2 sounding locations and times was used. Table  8  provides a summary of the models that were used. The models generally differed in their prior flux assumptions, prior flux uncertainty, transport model, initial conditions, spatial resolution, and inverse method, but had one commonality in that all assimilated in situ CO 2 concentration data. Because of these differences, the models often yielded a poste- riori X CO 2 fields that disagreed to some extent, with differences ranging from a few tenths of a ppm to several ppm as discussed below.
We used model output that covered a minimum period from September 2014 through December 2015, though a few models (CarbonTracker, TM5-4DVar) extended into March 2016. To compare against the models, for simplicity we computed only true X CO 2 values from the a posteriori CO 2 concentrations, rather than averaging-kernelcorrected values. Previous authors have shown that this effect is typically small, on the order of a few tenths of a ppm  (Wunch et al. , 2011a; Inoue et al. , 2013; Lindqvist et al. , 2015). For each matched OCO-2 sounding, the model median was computed from all available models for that sounding. If any model X CO 2 value differed by more than 1. 5 ppm from the model median, that sounding was excluded from our training data set. This requirement helped ensure that at the very least, all the models were generally consistent with each other for a given sounding in our training set.
Because bias correction affected this procedure, a preliminary filter set was first created, after which a preliminary bias correction was developed. The preliminary bias correction was then applied, δX CO 2 was updated accordingly, and the filters were re-derived using this bias-corrected δX CO 2. Generally this had only a minor effect on the filters, and often served to increase the fraction of data passed through filtering. Selection of thresholds for particular filters was somewhat subjective: generally bias was regarded as more problematic than scatter, but both were considered. Variables were typically selected as filters if they were correlated with bias greater than about 0. 5 ppm, or significant scatter (greater than about 2 ppm). The filtering variables and thresholds were derived separately for land (combined nadir and glint) and ocean soundings.
The reason for this is unknown, though we speculate that the actual parameter variability in the Southern Hemisphere is too small to obtain sensible slopes. Therefore, this truth proxy was excluded in the calculation of the final coefficients for glint soundings over water. Also, it is worth noting that the dP coefficient in B7 was roughly 3 times smaller than the value of -0. 23 adopted for B8. This was driven by inconsistencies in the B7 truth proxy data sets and a very small training data set, which yielded an unrealistically small value. Later analyses showed that B7 probably should have used a higher value, more in line with the B8 result.
Therefore, only relative land-ocean differences were used to infer C 0 in this "Model Bootstrap" method. As seen in Table  11 , the "bootstrap" methods were remarkably consistent with each other and with the direct TCCON overpass method, all suggesting an ocean scaling of roughly 0. 9955. X CO 2 ,tccon = X CO 2 ,OCO2 C 0 , (8) All of the methods described above for determining the global scaling of X CO 2 show remarkable consistency with each other, giving confidence that the overall scale of OCO-2 data is known to within a few tenths of a ppm, and the landocean difference is known to better than 0. 2 ppm. One caveat is that the direct TCCON overpass comparisons did not account for the averaging kernel correction in this analysis. After the release of OCO-2 X CO 2 B8, subsequent analysis showed that this correction typically lowered the value of TCCON by ∼ 0.
Based on these figures, B8 appears generally superior to B7 in terms of agreement with TCCON. First, there are more good-quality data in B7, at least for glint observations. This is primarily due to improvements in the prescreeners. In terms of accuracy, the scatter and outliers of X CO 2 over both land and ocean are reduced in B7, especially over ocean, for which the outliers were driven by Southern Hemisphere observations (in particular the high southern-latitude sites, Lauder and Wollongong). The bias over land in B7 was significant, with a 0. 3 ppm difference between land nadir and land glint. The overall apparent bias over land of 0. 2-0. 3 ppm is partly due to neglecting the averaging kernel effect when solving for the global divisors in the bias correction, and partly due to methodological differences in how we calculate the global bias in the first place.
While the R 2 values are significantly higher than those reported in  Wunch et al. (2017) , this is due to the extended length of the data record used here. Coupled with the secular increase in CO 2 , this leads to a larger dynamic range in X CO 2 and hence more signal and higher correlations than when using a shorter data record. Finally, we note that in B8 (and to some extent B7) there is generally a negative slope between TCCON and OCO-2. This appears to be due to a trend in OCO-2 X CO 2 relative to TCCON (not shown), in which OCO-2 appears to be losing X CO 2 at a rate of 0. 1-0. 2 ppm yr -1 , though this trend is not statistically significant. The apparent trend may be due to an OCO-2 calibration effect, and is currently under study. The general improvement in B8 over B7 can also be seen in comparisons to models.
Satellite retrievals are currently pushing that limit; for instance, the stated station-level calibration accuracy for TC-CON is currently 0. 4 ppm  (Wunch et al. , 2010). Calculations of OCO-2 station-level differences with TCCON show that the mean absolute bias at all stations with at least five valid overpasses is 0. 4 ppm, right at this level. Finally, we must have inversion and data assimilation systems that can make maximum use of these data. This requires, for example, minimizing transport model error, currently an active area of research (see, e. g. ,  Basu et al. , 2018; Schuh et al. , 2018). Significant progress has been made in the past decade in the retrieval of X CO 2 from SCIAMACHY, GOSAT, and OCO-2 radiances. This paper shows that this progress con-Appendix B: Land surface BRDF parameterization Since OCO-2 observations outside of target mode contain only single observation geometries, it is unrealistic to attempt to retrieve the BRDF shape on a per-observation basis.
Acknowledgements. Part of this work was conducted at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration (NASA)  for the Orbiting Carbon Observatory 2 project. Work at  Colorado State University  and the Geology and Planetary Sciences Department at the  California Institute of Technology  was supported by subcontracts from the  OCO-2  project. Car-bonTracker CT2015 and CT-NRT. v2016-1results provided by  NOAA ESRL, Boulder, Colorado, USA , from the website at https://carbontracker. noaa. gov(last access: 4 December 2018). Jena CarboScope atmospheric inversion results provided by  Christian Rödenbeck  and the  Max Planck Institute for Biogeochemistry , from http://www. bgc-jena. mpg. de/CarboScope/(last access: 4 December 2018).
Recruitment, Appraisal of Health, and Technology Adaptation Following the approval from the Research Ethics Board of the lead author's institution, participant recruitment was conducted in collaboration with eHealth Saskatchewan, which, with the support of Canada Health Infoway, had recently deployed a limited launch of the Citizen Health Information Portal (CHIP). This tethered patient portal was provided to approximately 1000 provincial residents facilitating access to data contained in their EHR. The portal allowed patients to add medical history information and provided views of laboratory results, immunizations, prescriptions, and hospital discharge summaries, as well as an opportunity to set reminders for medications and appointments  [28]. All CHIP participants received an email invitation to participate in the study.
No geographical limits were imposed during recruitment, but participants did need to be English speakers. The recruitment process resulted in a purposive sample of 26 participants for the study. Regarding the demographic data collection, participants were asked to self-identify their current health status. The primary means of accessing Web-based information was also assessed by self-report in this group. Finally, the research team used a modified adaptation of Rogers' diffusion of innovations theory  [29]  to explore participant comfort in adopting and using new technologies. Rogers used the following 5 stages to identify how innovations are transmitted and taken up by members of a social system: (1) Innovators, these individuals are typically risk-takers and lead the way when it comes to new technologies; (2) Early Adopters, includes individuals who will be among the first to try new technologies; (3) Early Majority, represents individuals who take time to consider trying out something new before acquiring and using it; (4) Late Majority, includes the group of individuals that is somewhat cautious about trying new technologies and will tend to adopt the use of technology more slowly than the average; and (5) Questioners, representing the last group that will uptake a new technology with users that XSL • FO RenderX typically require proof that new technology is worth investing in  [29].
Data Collection and Analysis Data collection was primarily achieved through semistructured interviews (Multimedia Appendix 1), and members of the research team also observed and collected data during a CHIP pilot participant focus group run by eHealth Saskatchewan. Once complete, the digital data files were transcribed, deidentified, and reviewed for accuracy, in preparation for analysis. In this study, interpretive description (ID) was used, a qualitative methodology first detailed by Thorne et al  [30]. ID was developed as a way of generating clinically relevant knowledge for health disciplines, and Thorne has since published, and recently updated, full text on this approach  [31]. Initially, a detailed line-by-line coding of the data was undertaken within the transcript documents to provide an initial sense of the scope of the data.
These early coding efforts revealed a wide diversity in the topic, enhanced by the line-by-line approach. The research team returned to the transcriptions to create a more focused dataset by collating all passages related to empowerment and engagement. In addition, the study team included any mention of either, or both, of these terms in the dataset based on the established interconnection of the concepts in the previously conducted scoping review  [27]. The dataset was prepared in a split-page word document format, and the research team returned to the coding process. Moving away from the granular line-by-line approach, the data were instead examined, as Thorne recommended, through an "exploration of commonalities and differences among and between individual experiences"  [32] , reflective memoing was used during the analysis along with regular interchanges between research team members to share emerging interpretations and code categories.
Acknowledgments The research team would like to thank the 26 study participants who volunteered their time to contribute to this work and provide new insights into patient empowerment in digital health. The study team also acknowledges the funding provided by eHealth Saskatchewan for this research, as well as the role of  Canada Health Infoway  in supporting the pilot delivery of the CHIP. In addition, we extend our thanks to the  University of Saskatchewan  for the provision of funds associated with the undergraduate summer research assistant program.
Data filtering The Gaia DR2 is unprecedented in both the quality and the quantity of its astrometric and photometric data. Still, this is an intermediate data release without a full implementation of the complexity of the processing for an optimal usage of the data. A detailed description of the astrometric and photometric features is given in  Lindegren et al. (2018)  and  Evans et al. (2018) , respectively, and  Arenou et al. (2018)  provides a global validation of them. Here we highlight the features that are important to be taken into account in building Gaia DR2 HRDs and present the filters we applied in this paper. Concerning the astrometric content  (Lindegren et al. 2018 ), the median uncertainty for the bright source (G<14 mag) parallax is 0. 03 mas. The systematics are lower than 0.
22 mag: parallax_over_error>10. Similarly, we apply filters on the relative flux error on the G, G BP , and G RP photometry: phot_g_mean_flux_over_error>50 (σ G < 0. 022 mag), phot_rp_mean_flux_over_error>20, and phot_bp_mean_flux_over_error>20 (σ G XP < 0. 054 mag). These criteria may remove variable stars, which are specifically studied in Gaia  Collaboration et al. (2018b). The processing of the photometric data in DR2 has not treated blends in the windows of the blue and red photometers (BP and RP). As a consequence, the measured BP and RP fluxes may include the contribution of flux from nearby sources, the highest impact being in sky areas of high stellar density, such as the inner regions of globular clusters, the Magellanic Clouds, or the Galactic Bulge. During the validation process, misdeterminations of the local background have also been identified.
Membership and astrometric solutions Two types of astrometric solutions were applied. The first type is applicable to nearby clusters. For the second Gaia data release, the nearby 'limit' was set at 250 pc. Within this limit, the parallax and proper motion data for the individual cluster members are sufficiently accurate to reflect the effects of projection along the line of sight, thus enabling the 3D reconstruction of the cluster. This is further described in Appendix A. 1. For these nearby clusters, the size of the cluster relative to its distance will contribute a significant level of scatter to the HRD if parallaxes for individual cluster members are not taken into account. With a relative accuracy of about 1% in the parallax measurement, an error contribution of around 0. 02 in the absolute magnitude is possible.
Selection of globular clusters The details of selecting globular clusters are presented in Gaia  Collaboration et al. (2018c). A major issue for the globular cluster data is the uncertainties on the parallaxes that result from the systematics, which is in most cases about one order of magnitude larger than the standard uncertainties on the mean parallax determinations for the globular clusters. The implication of this is that the parallaxes as determined with the Gaia data cannot be used to derive the distance moduli needed to prepare the composite HRD for the globular clusters. Instead, we had to rely on distances as quoted in the literature, for which we used the tables (2010 edition) provided online by  Harris (1996). The inevitable drawback is that these distances and reddening values have been obtained through isochrone fitting, and the application of these values to the Gaia data will provide only limited new information.