dataesr/scientific-paragraphs-categorization huggingface dataset:
2,14 A full list of investigators in the HERMES collaboration is included in the Supplement. Analyses were performed in 3 patient populations (all patients, early-treated patients, and late-treated patients). The early-treated population was defined as patients with an interval from last known well time to arterial puncture (LKWTP) of less than or equal to 4 hours. The 4-hour cutoff was selected based on spline analyses of both the HERMES clinical trial data set and the large Get With the Guideline-Stroke national registry indicating that the first inflection point of change in time-benefit associations from linear to nonlinear occurs at an LKWTP of 240 minutes. 4, 5 This morphologic change was deemed to potentially indicate the transition point at which substantial numbers of unwitnessedonset patients, patients with symptoms first observed on awakening, and patients with fast progression begin to not be included in the EVT population.
Race/ethnicity was not included because collection of race data was legally prohibited in some countries where studies were performed and because race/ethnicity is not a known major independent factor associated with outcome from large-vessel ischemic stroke. All effect size estimates are provided with their 95% CIs. All P values presented are 2-sided, with values less than. 05 indicating statistical significance, without adjustment for multiple comparisons. Statistical analyses were performed in SAS software, version 9. 4 (SAS Institute Inc), and R, version 3. 3 (R Foundation for Statistical Computing).
Discussion In this individual patient data pooled meta-analysis, substantial reductions in years of healthy life were associated in earlytreated patients with delays from last known well time to start of EVT procedures and to achievement of successful reperfusion and in both early-treated and late-treated patients with delays from door to procedure start and to reperfusion. Similar findings were noted for health-related quality of life at 3 months after stroke, as indexed by the UW-mRS. The timebenefit decrease was substantial: among early-treated patients, in the door-to-puncture time interval, each 1 hour of delay was associated with 0. 92 healthy life-years lost and each 1 second of delay with 2. 2 healthy life-hours lost. In contrast, no association of treatment delay with outcomes was noted for several care process intervals that have less precision of temporal measurement, less representation of fast as well as slow progressors, or both.
Limitations This study has limitations. The patient data come from randomized clinical trials with strict inclusion criteria that excluded patients with preexisting disability. Therefore, the outcomes of this analysis may not accurately reflect the changes of DALYs associated with EVT when applied to broader populations in routine practice, but they are fully applicable to patients who meet the highest level of guideline-endorsed eligibility for treatment, as these criteria reflect the clinical trial populations. 1  In addition, the analyzed trials had heterogeneous entry criteria and imaging methods. However, all pa- tients had disabling strokes with LVO and limited extent of early ischemic changes on baseline imaging. The utility scores used here were not directly collected from patients in each trial.
[11] [12] [13] [14] [15]  Given the importance of the topic, it is critical that decision-makers are able to understand what is known and knowable  [5, 16]  from observational data in COVID-19 policy, as well as what is unknown and/or unknowable. Motivated by concerns about the methodological strength of COVID-19 policy evaluations, we set out to review the literature using a set of methodological design checks tailored to common policy impact evaluation methods. Our primary objective was to evaluate each paper for methodological strength and reporting, based on pre-existing review guidance developed for this purpose. [17]  As a secondary objective, we also studied our own process: examining the consistency, ease of use, and clarity of this review guidance. This protocol differs in several ways from more traditional systematic review protocols given the atypical objectives and scope of the systematic review.
Statistical analysis Statistics provided are nearly exclusively counts and percentages of the final dataset. Analyses and graphics were performed in R. [25]  Krippendorff's alpha was calculated using the IRR package. [26]  Relative risks were estimated using the epitools package. [27]  Citation counts for accepted articles were obtained through Google Scholar  [28]  on January 11, 2021. Journal impact factors were obtained from the 2019 Journal Citation Reports. [29]  Data and code Data, code, the review tool, and the review guidance are stored and available here: https://osf. io/9xmke/files/. The dataset includes full results from the search and screening and all review tool responses from reviewers during the full review phase. After search and screening of titles and abstracts, 102 articles were identified as likely or potentially meeting our inclusion criteria.
Funding No funding was provided specifically for this research. Elizabeth Stone receives funding under the  National Institutes of Health  grant  T32MH109436. Ian Schmid receives funding under the  National Institutes of Health  grant  T32MH122357. Brooke Jarrett  receives funding under the  National Institutes of Health  grant  MH121128. Christopher Boyer  receives funding under the  National Institutes of Health  grant  T32HL098048  Cathrine Axfors receives funding from the  Knut and Alice Wallenberg Foundation , grant  KAW 2019. 0561. Beth Ann Griffin  and Elizabeth Stuart were supported by award number  P50DA046351  from the  National Institute on Drug Abuse. Elizabeth Stuart 's time was also supported by the  Bloomberg American Health Initiative. Caroline Joyce  receives funding from the  Ferring Foundation.
Westerly winds prevail, with mean wind speed of 4. 5 m s -1  (Madsen et al. , 2019 ). An Eddy-Covariance tower (EC) was set up at EastGRIP in 2016. The relevant variable measured from this system is latent heat flux (LHF) which is directly determined by the measurement of humidity fluxes between the surface and atmosphere. Positive LHF indicates upwards energy flux in the form of sublimation in Table  1. All field seasons had net sublimation, with the highest magnitude observed in 2019 (See Data Availability Section A).
Accumulation was calculated using the daily difference between measurements of snow height from each site. The resultant datasets consist of 10 daily measurements of three parameters, SSA, density and accumulation, over a 92, 100 and 66 day period for 2017, 2018 and 2019 respectively. Although samples were measured each day, the exact sampling time varies. Snow sampled during the afternoon would have had extended time exposed to solar radiation maximum, compared to snow sampled during the morning. Furthermore, the 95 sampling time has implications for capturing precipitation events. The table present the mean and standard deviation for the weather variables, surface temperature, relative humidity with respect to ice, wind speed and latent heat flux. Surface temperature and wind speed are from the PROMICE weather station based on hourly measurements during the field seasons of 2017, 2018 and 2019.
Relative humidity with respect to ice is calculated from vapour pressure of the air and saturation vapour pressure over ice. Latent heat flux is taken from the EC tower dataset. Wind Speed ( m s -1 ) 4. 9 ±. 2 4. 2 ± 1. 9 4. 5 ± 1. 6 Latent Heat Flux (W m -2 ) 1. 3 ± 4 1. 1 ± 3. 9 2. 6 ± 5. 9.
(  3 ), which follows the equation structure from Eq. (  1 ). dSSA dt = -0. 54 SSA + 14. 69 (2) SSA(t) = (SSA 0 -26. 8) e -0. 54t + 26. 8 (3) Where SSA(t) is the SSA measurement at a given time, SSA 0 is the initial SSA value, and -0. 54 m 2 kg -1 day -1 is the decay rate (α), as defined by the slope of Eq. (2). To account for a non-zero decay constant, the value 26. 8 m 2 kg -1 is defined by the value of x when the linear regression crosses the y-axis (Fig. 3a ). The SSA decay model describes rapid decrease in SSA based on empirical data from EastGRIP, Greenland.
Model evaluation Model performance is tested by comparing daily predicted decrease to the 10 daily observations. Model-data residuals for daily data are normally distributed, suggesting no systematic errors in model predictions. Figure  3  shows the construction of the model (Fig. 3 ,a-b) and prediction of SSA decay (Fig. 3 ,f-h). Note that events are here named E1,E2. consistent with Fig. 2  and also listed in Table  A1. There is a minor tendency for the model to underestimate the SSA decrease and thus overestimate the predicted values of SSA as seen in Fig. 3b. Model limitations are most evident during the first day, as seen in Fig. 3 , where the modelled decay Following our definition in Section 3. 1 the events have an extent of 2-5 days. To assess model performance in predicting magnitude of SSA decrease for events of different time periods, we compare the predicted versus measured SSA.
Environmental conditions during SSA decay events Intuitively, environmental conditions would be considered to play a role for surface snow metamorphism and the rate of SSA decay. To explore this, hourly weather measurements from the PROMICE AWS and field report weather observations are analysed to provide environmental context to SSA decay events. Weather station data shows no systematic influence of basic weather variables, relative humidity, surface temperature and wind speed on the model-data residuals, with linear regressions resulting in r 2 < 0. 1 for all variables. An overview of event conditions using field observations are presented in Table  A1. Temperatures below -25 • C are characterised by the same slope defined by the model (-0. 54 m 2 kg -1 day -1 ), but with a significantly higher intercept of 29 m s -1 day -1 compared to 14.
Decay model developments In this study, we present an empirical SSA decay model for surface snow of polar ice sheets based on continuous daily SSA measurements. The model describes SSA decay under natural summer conditions on the ice sheet. The findings from this study agree with previous studies, that SSA decay is most accurately described by an exponential function  (Cabanes et al. , 2002) , and indicates that the crystal structure of a new snow layer is a key driver of decay rate within the defined conditions over 2-5 day periods. Comparison with weather station data showed that the SSA decay rate during events had no systematic influence from weather variables (wind speed, temperature and relative humidity). The only exception is for temperatures outside the set range for the model. Surface temperatures below -25 • C were characterised by a significantly higher background SSA (defined as the mean SSA value of the final day of decay events) (Fig.
No clear seasonal trend is observed in d-excess (Fig. 4b) but with periods of gradual decreases. Total daily spread in δ 18 O and d-excess is 15 ‰. During 2017, 2018 and 2019 SSA has one dominant mode of variance (PC1) explaining 61 %, 77 % and 72 % of the total variance in the respective datasets. PC1 of δ 18 O explains 69 %, 83 % and 75 % of the total variance for the respective years. While PC1 of d-excess explains 47 %, 51 % and 60 % for 2017, 2018 and 2019 respectively. PC1 of δ 18 O and d-excess show strong coherence from May to early June during 2017 and 2018, while for the second half of the season, and throughout 2019, PC1 of d-excess corresponds to PC1 of SSA (Fig. 4d). Surface variability due to post-depositional reworking of the snow is shown by a wide spread in SSA values during a given day.
Time periods with low spatial variability indicate largely homogeneous snow cover over the transect, shown in Fig. 4 as shaded regions. High variability is defined by periods where the 5-day running-mean of spatial variance in δ 18 O is greater than one standard deviation. During periods of low spatial variability in isotopic composition, there is greater coherence between PC1 of SSA and PC1 of d-excess, due to a reduction of noise in the dataset. PC1 of SSA and d-excess show a coherence.
430 Snow isotope data is also available on the PANGAEA database with the DOI:***. Data from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) 400 were provided by the Geological Survey of Denmark and Greenland (GEUS) at http://www. promice. dk. Eddy Covaraniance Tower measurement are available on the PANGAEA database with the DOI: https://doi. org/10. 1594/PANGAEA. 928827.
The LINAC and the collimator surrounded by the neutron shielding are shown in Fig. 1. A remotely controlled mechanical bench, usually dedicated to high-energy photon imaging of waste drums  [11]  , transfers the concrete mock-up slice (Fig. 2a ) from the irradiation position to the counting position, which is distant from the LINAC to limit fast photoneutron damages to the HPGe crystal. Delayed gamma rays are recorded with a 50 % relative efficiency n-type coaxial HPGe detector (ORTEC GMX50-83-A-PL) equipped with a transistor-reset preamplifier. Acquisition is performed with a LYNX Digital Signal Analyzer driven by the Genie2000 software (MIRION Technologies), which also allows gamma spectra analysis. Dead-time is taken into account with the livetime correction method. The energy resolution is 2.
3 , counting position 1). After a 45 min acquisition, a backward 45° rotation puts back the uranium sample in front of the HPGe detector (see Fig. 3 , counting position 2, which is the same as during irradiation), and delayed gamma rays are counted during a few hours (precise acquisition times are given in Table  II ). The delayed gamma spectra are analyzed with Genie2000 software to extract the net peak areas of the photofission products of interest. For the localization, we are interested in the ratio of two gamma rays emitted by a same photofission product to exploit the differential attenuation of its gamma rays at different energies, when crossing different thicknesses of concrete. The localization combines experimental data with Monte-Carlo simulations performed with MCNP 6. 1 computer code  [12].
Its relative uncertainty is calculated as the quadratic combination of the uncertainties on its two gamma-ray intensities, found in JEFF-3. 3 database  [13]. The relative uncertainty on the measured ratio corresponds to the quadratic combination of the statistical uncertainties on the net peak areas of the gamma rays and on the detector intrinsic efficiency estimated to 2% with an optimized MCNP model of the HPGe detector. Finally, the relative uncertainty on the corrected ratio is dominated by that of the measured ratio, the uncertainties on the mass attenuation coefficient and concrete thickness being smaller. Finally, the discrepancy between the real and corrected ratios is at most a few percent for the 2218. 0/2639. 6 keV ratio of 138 Cs, by using the localization provided with the 1435.
Recently, the Trauma-INTACT trial (ClinicalTrials. gov: NCT03875937) assessed the PK of TXA administered intramuscularly in bleeding trauma patients  14. The study found that the time to reach therapeutic concentrations of 10 mg/L after intramuscular injection of 1 g TXA was within 15 min. Additionally, it was well tolerated with only mild injection site reactions and no serious adverse events reported. The PharmacoTXA trial (ClinicalTrials. gov: NCT03777488) has assessed the PK of TXA administered intramuscularly or orally in healthy volunteers. An ongoing RCT called TRACES (TRAnexamic Acid to Reduce Blood Loss in Hemorrhagic CESarean Delivery) aims to assess the PK and pharmacodynamics (PD) effects of two doses of TXA (0. 5 and 1 g) injected intravenously in women with an ongoing PPH after delivery  15.
Randomisation After collecting baseline data, enrolled participants will be randomised into one of the four groups described in "Description of investigational product and justification of the dosage, route of administration, administration schedule and treatment duration". The sequence for each dosing cohort will be created using computer-generated random numbers, using blocking to ensure the required balance in the allocation of participants to treatment arms. The allocation ratio will be 1:1:1:1. An IT coding expert supported by a statistician who are not involved in the conduct of the trial will prepare the randomisation codes. Eligibility will be confirmed and randomisation done via an online database. Once the code is generated, the trial intervention will be prepared and administered by a trained trial team member.
Premature exit of trial participant Participants may exit the study at any time and for any reason. A previously given consent can be withdrawn. The investigator can withdraw a participant from the trial for any safety reason or if it is in the participant's best interest. If a participant exits the trial prematurely or withdraws consent or refuses consent for continuation, data collected up to time of premature exit will be used. Participants who withdraw from the trial with less than six post treatment PK samples will be replaced to ensure the four intervention groups remain balanced.
Statistics and data analysis Sample size calculation Using PFIM 3. 2. 1 software 51 and based on the population pharmacokinetic parameters determined in a meta-analysis of the different pharmacokinetic studies published in healthy volunteers and data through the IV route in trauma patients  13, 52  , a sample size of 120 participants will allow estimates (relative standard errors < 30%) of the pharmacokinetic parameters of the intravenous, intramuscular and oral administration of TXA. Optimal maternal blood sampling times were evaluated and are as follows: immediately before TXA administration, and 15, 30 minutes, 1h, 2h, 4h, 8h, 12h and 24h after TXA administration. However, if in exceptional circumstances a sample cannot be taken at the optimal stated time, flexibility in blood sampling is allowed as detailed in the section "TXA administration and timing of biological samples".
Statistical analysis plan All statistical calculations will be performed using STATA, unless otherwise specified. All data will be presented in the form of summaries sorted by treatment group (cohort) and participant ID. Tabular summaries will be presented based on the following grouping: IM, IV, Oral solution and No TXA. For continuous variables, summary statistics will include sample size, mean, standard deviation, median, minimum, and maximum values. Frequencies and percentages will be calculated for categorical variables.
Pharmacokinetic analysis All participants who receive the full dose of TXA and did not vomit the oral dose within the first hour and have at least six PK samples obtained after TXA administration to determine maternal plasma concentrations of TXA will be included in the PK data analysis. The actual blood sampling times will be recorded and used in calculations for PK parameter estimation. TXA time-courses will be analysed using the nonlinear mixed effect modelling software program Monolix 2020R1 version  53  , as previously described  52, [54] [55] [56]. Briefly, parameters will be estimated by computing the maximum likelihood estimator of the parameters without any approximation of the model (no linearization) using the stochastic approximation expectation maximization (SAEM) algorithm combined to a Markov chain Monte Carlo (MCMC) procedure (to ensure full convergence, the MCMC will be fixed to 20 and the iteration number to 1000).
Data confidentiality The persons responsible for the quality control of the study will take all necessary precautions to ensure the confidentiality of information relating to the IMP, the study, the study participants and in particular the identity of the participants and the results obtained. These persons, as well as the site investigators themselves, are bound by professional secrecy. During and after the clinical study, all data collected about the study participants and sent to LSHTM CTU by the investigators (or sent to other collaborators) will be anonymised. Under no circumstances will the names and addresses of participants be shown. The trial will comply with relevant Data Protection regulations including the European Union General Data Protection Regulation. The Sponsor will ensure that appropriate consent is in place to access any personal information about the participant which is necessary for the quality control of the study.
Members of the The DMC will review on a regular basis accumulating safety data (adverse events and injection site reactions) from the ongoing trial, and advise CIs regarding the continuing safety of current participants and those yet to be recruited. Data on the type, frequency and severity of adverse events in mother and neonate will be reported to the DMC. The DMC membership includes expertise in clinical trials, the use of TXA in obstetrics and gynaecology, care of women during childbirth and postnatal period and care of the newborn. The committee members are familiar with the safety profile of the drug. The DMC Charter includes, but is not limited to, defining: • the schedule and format of the DMC meetings; • the format for presentation of data; • the method and timing of providing interim reports.
However, they compare their data with those from Hess Deep and suggest that the formation of the latter may have occurred by subsiding crystal mush in the shallow part of the lower crust, and by in-situ crystallization deeper in the crust. Van-Tongeren et al. (2015)  studied plagioclase CPO along the entire lower crust in Wadi Khafifah (Wadi Tayin block, Samail ophiolite) and found no down section trends, neither in fabric strength nor in lineation orientation that are predicted in the gabbro glacier model. Hence, their data support the sheeted sill model for the studied crustal section. Seismic experiments performed at the Juan de Fuca Ridge  (Canales et al. , 2009) , the Galapagos Spreading Center  (Boddupalli & Canales, 2019)  and the EPR  (Marjanovic et al. , 2014 (Marjanovic et al. , , 2015) )  provide evidence for the occurrence of small melt sills within the lower crust supporting the possibility of in-situ crystallization beneath the spreading center.
, 2014; Koepke et al. , 2017; Müller, 2016; Müller et al. , 2014; Oeser et al. , 2012). Our data provide a high spatial resolution along the entire gabbroic section exposed at Wadi Gideah. A further increase of the spatial resolution will also be reached in upcoming publications by integrating drill core data in the frame of the Oman Drilling Project (see below;  Kelemen et al. , 2020). For the drill sites within the layered gabbro and in the transition between layered and foliated gabbro, the Wadi Gideah was chosen because it displays a continuous crustal section from the crust/mantle boundary to the sheeted dike horizon in a region (the southern Wadi Tayin massif) that is arguably not disturbed by any secondary magmatic event such as large intrusions of wehrlites or gabbronorites (e. g.
Geological Background: The Wadi Gideah Reference Section The samples investigated here are a subset of a sample suite collected over four field campaigns along Wadi Gideah in the Wadi Tayin block of the Samail ophiolite (e. g. ,  Nicolas et al. , 2000; Pallister & Hopson, 1981 , Figure  1 ), with the aim of constructing a complete analytical data set for a reference section in an analog to fast-spreading centers  (Garbe-Schoenberg et al. , 2014; Koepke et al. , 2017; Müller, 2016; Müller et al. , 2014; Oeser et al. , 2012). Beside the microstructural data presented herein, the results of detailed petrographic and petrological, trace element and isotopic, as well as crystallization temperature studies will be published soon qualifying the Wadi Gideah section as a reference section of lower fast-spread crust in the Samail ophiolite.