dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design The ABC PICU trial is a large multicenter, international, double-blind, superiority, two-arm RCT. It will compare the risk of New or Progressive Multiple Organ Dysfunction Syndrome (NPMODS) between patients transfused RBCs of decreased storage age (length of storage ≤7 days) and those transfused standard-issue RBCs (stored for 2-42 days; expected average length of storage of about 17-21 days). The sample size of the trial is 1538 children and we are enrolling from a wide variety of pediatric hospitals in the US, Canada, France, Italy, and Israel. A summary of the protocol is provided in Table  1  (World Health Organization trial registration dataset) and in Additional file 1 (Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) Checklist).
Clinical and outcome information A schedule of enrollment, interventions and assessments is reported in Fig. 2. ICU data collected is listed in Table  3. Baseline data at admission includes co-morbidities, type of ICU admission, blood type, and hemoglobin prior to first RBC transfusion. Clinical and outcome data, as well as the Pediatric Logistic Organ Dysfunction version 2 (PELOD-2) score and specific MODS information, are collected daily through day 7 after randomization, then on days 14, 21, Table  1  World Health Organization trial registration dataset (Continued) post-conception age < 36 weeks on admission to ICU; documented RBC transfusion within the 28 days prior to fulfilling the eligibility criteria; previously randomized in this study; weight < 3. 0 kg on ICU admission; pregnant; conscious objection or unwillingness to receive blood products; not expected to survive beyond 24 h, brain death or suspected brain death; limitation or withdrawal of care decisions have been made; enrollment in another randomized clinical trial which has not been approved for co-enrollment; patients for whom autologous and/or directed donation RBCs will be provided; patients for whom the treating physician routinely and systematically requests RBC ≤ 14 days of storage; patients for whom there systematically exist RBC aliquoting policies that mandate the initial use of units stored for ≤ 14 days; on ECMO or plan to be immediately placed on ECMO at time of enrollment; patient predicted or presumed to require a massive transfusion (> 40 ml/kg of all blood components in a 24-h period) according to treating physician judgment; refusal by physician; inability to obtain consent; blood bank personnel experiences difficulties in securing blood products (difficult cross matches, rare blood groups, and diseases like IgA deficiency); insufficient number of ABO type compatible RBC units available in the blood bank at randomization with a storage time ≤ 7 days (minimum.
In France, blinding occurs by re-printing a blood-bag label on which the expiry/collection date was no longer showing. Accidental un-blinding of the RBC unit expiration date is documented and reported via the protocol deviation process. The data of study patients with accidental un-blinding will be included in the intent-to-treat (ITT) analysis. Outcome evaluation, diagnosis of NPMODS and the determination of all scores are done by research assistants who are unaware of treatment allocation.
Statistical analyses Baseline characteristics Baseline characteristics of patients, intervention, and co-interventions in both study arms will be assessed using frequency distributions and univariate descriptive statistics including measures of central tendency and dispersion. Mean (± standard deviation) and median (interquartile range) will be used to report data as appropriate. Percentages will be reported for categorical data. Any known clinical risk factor, whether or not there is a statistically significant imbalance, will be considered for adjusted analyses of primary and secondary outcomes.
Analysis of the primary outcome measure As this is an effectiveness trial, the analysis of the primary outcome measure will be conducted on an ITT approach. Data from all participants enrolled will be analyzed according to the intervention to which they were allocated, regardless of whether it was received or not (Fig. 3 ). The primary outcome (i. e. , the effect of treatment, short storage versus standard issue, on development of NPMODS), will be analyzed using an unadjusted chi-square. The principal effect measure will be an unadjusted relative risk reduction with a 95% CI. A per-protocol analysis of the primary outcome measure will also be done. Sensitivity analysis will be performed that excludes patients in the short-storage group who receive RBCs stored for > 7 days and patients in the standard-issue arm who receive RBCs stored for ≤ 7 days.
Interim analysis The DSMB of the ABC PICU trial requested that an interim analysis be done when enrollment and completed data collection reached 50% of recruitment targeted. The interim statistical analysis will compare NPMODS rates in the short-storage and standard-issue groups, using O'Brien-Fleming stopping rules  [43] , with a two-tailed p value. Stopping rules are based upon safety concerns as assessed by the DSMB. The DSMB of the ABC PICU trial could consider terminating enrollment if the statistical analysis showed a statistically significant difference. Both positive and negative findings from the ABC PICU trial are considered of clinical interest.
Study monitoring The DSMB for ABC PICU was established by the National Heart Lung and Blood Institute (NHLBI) to monitor data and oversee patient safety in this study and convenes twice a year. The principal investigators, staff from the DMC and NHLBI participate in the meetings as non-voting members. Study monitoring methods vary according to country. For all participating centers throughout the US, Canada, and Europe, 100% of eCRFs are audited through automated query process using pre-determined ranges flagging suspicious or out-of-range values. For US participating centers, as well as for Italy and Israel, there are a total of three scheduled site visits (with the exception for those sites that on-boarded in 2017 where there may only be one or two visits due to time restraints). These visits are undertaken following the enrollment of, and completion of, 28-day data of the first two randomizations at the study interim, and a visit is planned at study close-out.
Close-out and access to data All data and source documentation will be stored in a secure storage facility for 7 years from the time of study close-out. Datasets must be submitted to the study NHLBI study program official no later than 3 years after the end of the clinical activity (final patient follow-up, etc. ) or 2 years after the main paper of the trial has been published, whichever comes first. Data is prepared by the study coordinating center and sent for review prior to release. The NIH and NHLBI expect the timely release and sharing of data to be no later than the acceptance for publication of the main findings from the final dataset.
World Health Organization trial registration dataset Hôpitaux de Paris, Ministère des Solidarités, de la Santé et de la Famille, France; 4. Ministère des Affaires Sociales et de la Santé, Paris, France (PHRC 14-0390); 5. The Ministère de la Santé et des Services Sociaux de la Province de Québec; and 6. Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA.
Acknowledgements We thank the members of the Data and  Safety Monitoring Board  of the  ABC PICU study , ( Drs J. Michael Dean ,  Jeffrey L. Carson ,  Harvey Klein ,  Darrell J. Triulzi ,  Brett P. Giroir ,  Sarah Hoehn , and  Maria Mori Brooks ),  National Heart Lung and Blood Institute. The  ABC  PICU study is conducted by  Philip C. Spinella ,  Marisa Tucci , and  Jacques Lacroix , and by two study managers ( Lucy Clayton  in Montreal and Tina Bockelmann in St. Louis). Data management is supervised by  Dean Fergusson  and  Dong Vo , and is carried out by the Methods Center of the  Ottawa Hospital Research Institute  and the  Faculty of Medicine, University of Ottawa ,  Ottawa. Oversight  of data management as well as reporting of adverse events and query management is carried out by  Ken Schechtman ,  St.
Funding This study is funded by (1) The  National Heart, Lung and Blood Institute  (Grant # 1U01HL116383-01 ), ( 2) The  Canadian Institutes of Health Research  (grant # 126113 ),  Ottawa, ON, Canada, (3) Comité National de la Recherche Clinique, Département de la Recherche Clinique et du Développement (DRCD) ,  Assistance Publique -Hôpitaux de Paris, Ministère des Solidarités, de la Santé et de la Famille, France, (4) Ministère des Affaires Sociales et de la Santé, Paris, France  ( PHRC 14-0390 ),  (5) Ministère de la Santé et des Services Sociaux de la Province de Québec , and (6) The  Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA.
In an increasing hydro-climatic risk context as a result of climate change, this work aims to identify future hydro-hazard hot-spots as a result of climate change across Great Britain. First, flood and drought hazards were defined and selected in a consistent and parallel approach with a threshold method. Then, a nation-wide systematic and robust statistical framework was developed to quantify changes in frequency, magnitude, and duration, and assess time of year for both droughts and floods, and the uncertainty associated with climate model projections. This approach was applied to a spatially coherent statistical database of daily river flows (Future Flows Hydrology) across Great Britain to assess changes between the baseline  and the 2080s (2069-2098). The results showed that hydrohazard hot-spots are likely to develop along the western coast of England and Wales and across north-eastern Scotland, mainly during the winter (floods) and autumn (droughts) seasons, with a higher increase in drought hazard in terms of magnitude and duration.
The Future Flows Hydrology dataset The Future Flows Hydrology (FFH) database  (Prudhomme et al. , 2013)  is currently the only nation-wide, consistent, probabilistic future transient hydrological projection available for the UK. Future Flows Hydrology is derived from the Future Flows Climate  (Prudhomme et al. , 2012b) , a national, 11-member ensemble projection derived from the UK Met Office Hadley Centre's HadRM3-PPE ensemble projection. HadRM3-PPE-UK was developed as part of the derivation of the UKCP09 scenarios  (Murphy et al. , 2007)  and designed to represent parameter uncertainty in climate change projections through a parameter variant experiment, and was run under the SRES A1B emissions scenario (see  Murphy et al. , 2009 , which details the climate model perturbations). Future Flows Climate was used as forcing for three hydrological models (CERF -  Griffiths et al.
, 2006; PDM -Moore, 2007; and CLASSIC -Crooks and Naden, 2007)  to create the Future Flows Hydrology database, which contains an 11member ensemble of transient projections of daily river flow for 281 catchments from January 1951 to December 2098. Each FFH member is associated with a single realization from a different variant of HadRM3, each member representing an equally probable, plausible realization of the future  (Murphy et al. , 2007).
1a ) were extracted. Drought characteristics (frequency, magnitude, duration, and time of year; see Table  1 ) were analysed following the method from Gustard and Demuth (2009) using R package "lfstat" (available at https://cran. r-project. org/web/packages/ lfstat/index. html, last access: 10 October 2018, R version 3. 4. 4 and lfstat package version 0. 9. 4). As for floods, each ensemble member was treated independently. Here, after a sensitivity analysis on drought event frequency, a daily varying Q90 threshold (i. e. the flow which was equalled or exceeded 90 % of the time over each Julian day across the 30year baseline) was applied to select on average three independent low-flow events per year on the baseline (see grey line in Fig. 1b ). Dependent events were pooled together applying the IC method (Gustard and Demuth, 2009) using a minimum of 5 days' inter-event time, and a 0.
Hydro-hazard hot-spots Figure  4  shows the catchments identified as hydro-hazard hot-spots in Great Britain for the 10th (Fig. 4a ), 50th (Fig. 4b ), and 90th (Fig. 4c ) percentiles across the 11 ensemble members of the Future Flows Hydrology database. Only two catchments were identified for the 10th percentile (Fig. 4a ) in Wales (Gwili River at Glangwili) and Scotland (Ruchill Water at Cultybraggan). For the 50th percentile, representing the median trend across the 11 climatic projection ensemble members, 48 catchments are defined as hot-spots (Fig. 4b ), mainly located on the western coast and in the northeast of Scotland. The 90th percentile shows 135 catchments (Fig. 4c ) spread throughout Great Britain. These hotspots are the result of a combination of changes in drought and flood characteristics, which are detailed in the following sections.
Understanding hydro-hazard hot-spots British hydro-hazard hot-spots are identified mainly along the western coast and in northern Scotland. Indeed, results show a marked northwest-southeast gradient across Great Britain for changes in both droughts and floods according to the FFH database. The western coast shows smaller but more likely increases in flood hazard in the 2080s (in terms of frequency, magnitude, and duration), and a higher increase in magnitude and duration in drought hazard from the baseline to the 2080s. In the baseline (see Fig. 9 ), the seasonality of droughts is very low (below 0. 2 for all the stations of the 10th and 50th percentiles and below 0. 4 for all the stations of the 90th percentile), showing that the mean day of year is not representative of these events, while for floods seasonality is high (above 0.
, 2017) , which is consistent with the spatial distribution of results in this study. These future changes would be the continuity of observed trends found in the literature. Harrigan et al. (  2018 ) showed a significant increase in observed high flows over 1965-2014 across near-natural catchments in the United Kingdom, particularly in Scotland, which is explained by wetter winter and autumn seasons, and  Blöschl et al. (2017)  showed temporal shifts of observed floods to an earlier winter season in Scotland and northern England. The changes in low flows are highly constrained by the Future Flows Climate (FFC) dataset, which was used to generate Future Flows Hydrology  (Prudhomme et al. , 2012b). Drought propagation from the meteorological signal to the hydrological signal can show a fair linearity in temperate climates, such as the British oceanic climate, being mainly driven by precipitation and temperature patterns  (Van Loon et al.
, 2014) , particularly for catchments with low influence from groundwater dynamics. For the medium scenario (A1B), the UKCP09 projections in the 2080s result in winter precipitations that suggest a higher increase in the southeast of England than on the western coast, while future summer precipitations range from a significant decrease to a slight increase, with a wider uncertainty in the south and southeast of England. Rahiz and New (2013)  analysed changes in monthly precipitation series of the HadRM3-PPE-UK database, the same ensemble of regional climatic projections that were then downscaled to create the FFC dataset. Maps of drought intensity (DI) in the 2080s calculated based on the 6-month drought severity index show that the increase in hydrological drought found in their study for the west of Great Britain is mainly explained by an increase in DI in summer.
According to the FFH database no particular time of the year is emphasized on the baseline. However, when compared to the baseline, results suggest that there would be more water deficit in autumn and winter in the 2080s. These trends could result in multi-seasonal drought (or "wet-to-dryseason drought", as defined by  Van Loon and Van Lanen, 2012)  if occurring after a significant summer low flow, as recharge would not fully recover in winter as expected, which could also result in a more severe low flow in the following summer season. Indeed, in England and Wales winter rainfall is key to groundwater recharge, which is the principal source of river flow in summer, showing these regions are particularly vulnerable to winter droughts  (Marsh et al. , 2007). These results suggest a need for adapting water management policies in light of climate change impact, not only on the magnitude, but also on the timing of low-flow events.
Limits of the study Some limits of this study are related to the use of the Future Flow database. As reported by  Prudhomme et al. (2013) , three hydrological models are used to simulate river flow with the emphasis of calibration on different parts of the flow regime. The CERF model was calibrated mainly on the representation of the water balance and low flows, while for PDM and CLASSIC the emphasis is on the upper part of the flow regime and peak flows. For the gauging stations calibrated with the CERF model, the high flows might thus be underestimated, while for the gauging stations calibrated with the PDM and CLASSIC models, the low flows might be overestimated. Moreover, this study investigates the uncertainty related to one climate model only (HadRM3), under one forcing scenario (SRES A1B).
The FFH database is based on a downscaled subset of the UKCP09 database, the HadRM3-PPE-UK, which does not capture the full range of the climate variable space projected by UKCP09  (Prudhomme and Williamson, 2013). For example, when using outputs from the UKCP09 weather generator  (Murphy et al. , 2009)  with a range of different emission scenarios, changes in peak flows show a different spatial distribution (higher increase in 1 : 20-year return period events in the west), with a wider uncertainty  (Kay et al. , 2014a, b). However,  Prudhomme et al. (2012c, Sect. IV)  showed that the statistical range and distribution of hydrological changes in the FFH was generally consistent with the fuller uncertainty described by UKCP09derived hydrological changes for the 2050s horizon. Investigating hydrological data derived from climatic projections forced by a wider range of emission scenarios would thus probably lead to a larger range of possible changes in high and low flows  (Wilby and Dessai, 2010).
Conclusion and perspectives In the context of increasing hydro-climatic risk arising from climate change, this paper aims to characterize the changes in flood and drought hazards spatially, temporally, and by magnitude in a consistent and parallel approach. It also embraces the uncertainties related to climatic projections and provides a framework to quantify the likelihood of these changes. A systematic approach is thus developed and applied to a spatially coherent statistical database of daily river flows (Future Flows Hydrology) across Great Britain to assess changes between the baseline  (1961-1990) and the 2080s (2069-2098). This method characterizes changes in frequency, magnitude, duration, and time of year of flood and drought hazards consistently, and identifies future hot-spots across the country.
Results showed that the FFH projects hydro-hazard hot-spots along the western coast of England and Wales and in Scotland, mainly during the winter (floods) and autumn (droughts) seasons, with a higher increase in drought hazard in terms of magnitude and duration. Some limits to this study relate to the ability of the hydrological models (used to produce the FFH database) to reproduce extreme high and low flows, while others are associated with FFH's limitation to one single climate model and emission scenario (SRES A1B). However, this paper sets out a novel approach to characterize both flood and drought hazards in a consistent manner across a large territory and in a probabilistic framework. This paper presents a robust methodological approach to identify hydro-hazard hot-spots over a large spatial domain.
The FFH database is a unique spatially coherent nationalwide statistical river flow database, and presents an opportunity to develop methods to quantify climate change impacts (and its associated uncertainty) on hydrological extremes. It can be transferred to other large-scale statistical hydrological products that are emerging, such as the End-to-end Demonstrator for improved decision-making in the water sector in Europe experiment (EDgE, http://edge. climate. copernicus. eu/, last access: 10 October 2018), which shows a growing interest towards large-scale impacts of climate change on the hydrological cycle by stakeholders and a need for practical end-user available data on this matter. Moreover, the upcoming UKCP18 (http://ukclimateprojections. metoffice. gov. uk/24125, last access: 10 October 2018) based on IPCC AR5 should also provide appropriate downscaled climatic projections for the UK, the development of these new climate scenarios being driven by both the climatic and the end-user communities.
This work is not an attempt to present the most state-of-the-art climate change projection chain, but rather to develop a novel methodological approach to characterize changes in both hydrological extremes as a result of climate change. This method is thus now transferable to these upcoming new databases to understand climate change impact on hydro-hazards and ultimately inform stakeholders and decision-makers. The output maps can be used to select case studies and investigate changes in floods and droughts in a risk assessment framework. Moreover, the effect of different catchment characteristics like urbanization and soil type on hydro-hazards could explain the changes in future projections. Recent work (e. g. Miller and Brewer, 2018)  suggests that urbanization may have a significant effect on runoff characteristics and hence on flood and drought risks.
Data availability. The Future Flows Hydrology dataset is associated with digital object identifier https://doi. org/10. 5285/f3723162-4fed-4d9d-92c6-dd17412fa37b. This must be referenced fully for every use of the Future Flows Hydrology data as Haxton T. , Crooks S. , Jackson C. R. , Barkwith, A. K. A. P. , Kelvin, J. , Williamson, J. , Mackay, J. D. , Wang, L. , Davies, H. , Young, A. , and Prudhomme, C. : Future Flows Hydrology, https://doi. org/10. 5285/f3723162-4fed-4d9d-92c6-dd17412fa37b, 2012. All Future Flows Hydrology files are available through the CEH Environmental Informatics Data Centre Gateway under special licensing conditions (https://gateway. ceh. ac. uk/, CEH, 2018a, or https://doi. org/10. 5285/bad1514f-119e-44a4-8e1e-442735bb9797). They are also available through the National River Flow Archive (http://nrfa.
ceh. ac. uk/data/search?db=nrfa_ public&stn=categories%3A%2AFUTUREFLOWS%2A, CEH, 2018b) and the National Groundwater Level Archive (http://www. bgs. ac. uk/research/groundwater/change/FutureFlows/home. html, British Geological Survey, 2018) where metadata associated with each study site and hydrological observation can be found. Future Flows Hydrology is available under a licensing condition agreement. For non-commercial use, the products are available free of charge. For commercial use, the data might be made available conditional on a fee to be agreed to with NERC CEH and NERC BGS licensing teams, owners of the IPR of the datasets and products.