dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, data from 255 patients were analyzed. Of note, data from 57 patients were already published  [12]. However, in the previous publication, HEV RNA polymerase mutations were not analyzed. For this reason, data from these patients were included for the present study. Details regarding participating centers and the number of included patients per center are presented in the Supplementary Materials. The patients' characteristics are presented in Table  1. Data were recorded anonymously. According to French and German laws, anonymous retrospective studies do not require institutional review board (IRB) approval. In other countries, the study was approved by the IRB of each hospital.
Statistical Analyses Data are presented either as mean ± standard deviation or median (range). Proportions were compared by the χ 2 test or Fisher exact test. Quantitative variables were compared by the nonparametric Friedman test for serial measurements and either Student t test or the Wilcoxon test. Independent factors associated with a sustained virological response were studied using a stepwise multivariate logistic regression model that used initial inclusion criteria that had a significance of P <. 05, using Statview software (SAS Institute, Cary, North Carolina). For this purpose, patients with an SVR were compared to those without an SVR (as defined above). A P value of <. 05 was considered to be statistically significant.
In the present study, HEV RNA was not systematically assessed in the stools at the end of therapy. In our previous multicenter study, in univariate analysis, we found that positive HEV RNA in the blood after 1 month of therapy was associated with SVR  [12]. This was not confirmed in the present study. Finally, in a small case series that included 35 patients, we observed that a decrease of HEV RNA concentration ≥0. 5 log at day 7 after starting ribavirin was an independent predictive factor for SVR  [27]. In the present study, data regarding HEV RNA at day 7 were not collected. Due to its retrospective nature, our study has several limitations. The duration of ribavirin varied considerably across different centers. We cannot exclude that in some patients the duration of ribavirin was determined according to their immunological status or to the evolution of viral load under ribavirin, especially those who were given >6 months of therapy.
This offers interesting comparisons. We used a similar heuristic approach to the one used by other authors regarding quality in health systems policy  [27] , and searched the websites of the two major global institutions shaping UHC policies and in charge of its global monitoringthe WHO and the World Bank (WB)for reports and policy documents dedicated to UHC. We selected the most relevant and significant ones to build a purposeful sample of 20 key UHC reports and policy documents issued after the release of the first World Health Assembly Resolution on UHC  (58. 33)  in 2005 (actually starting in 2008) and until 2018. Note that we excluded two documents whose primary focus was to comprehend equitythe report of the Commission on social determinants of health  [17] , and the WB's database of equity indicators  [28]  but we also identified and included a report of the International Labour Organisation (ILO) dedicated to social protection in health  [29].
Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the populationattributable burden of locoregional and distant recurrence in breast cancer patients.
Estimation of P AF (l, h) For estimation, the LMs should be chosen based on the number of exposed and unexposed patients at each time point. The number in both patient groups should be sufficiently large for inference. The time window of fixed length can be defined based on clinical knowledge and relevance. Examples of how to choose the LMs and time windows are presented in Sections 5 to 7 based on a simulation study and real data examples. Inference at a LM l is performed on a LM dataset (  [12] ) which consists of all patients who are still at risk at the LM. For every patient within the LM dataset the exposure state at the LM as well as the event state at the end of the time window is accessed. Patients who do not experience the event of interest within the time window are implicitly assumed to be either still at risk at the end of the time window or to have experienced the competing event.
P (D l,h = 1|A l = 1, E l , Z l ) = exp(β 0l + β 1l × E l + β T l × Z l ), (6) where β 0l is the intercept at LM l, β 1l is the coefficient of the exposure state at l, β T l is the vector of coefficients of Z l. Z l is a vector of baseline and timedependent covariates (with covariate values fixed at time point l) sufficient for confounder control. The condition A l = 1 is automatically fulfilled, as estimation is based on the LM dataset. The RR l,h is estimated by exp( β 1l ). The log-model is implemented in the statistical software R within the function glm (  [17] ). The prevalence P E l is simply estimated by the proportion of exposed subjects among cases. With glm, P E l could, for example, be estimated by P E l = P (E l = 1|A l = 1, D l,h ) = exp(α 0l + α 1l × D l,h ), (7) where α 0l is the intercept at LM l and α 1l the regression coefficient of D l,h.
The methods are well described by  [19]. The downside of this approach is that the curve may be easily overfitted, as the choice of the degree of smoothing is not based on any statistical tests. Therefore, the gain in efficiency is negligible. As an alternative the principle of pooled logistic regression by a so-called supermodel can be used to obtain a smooth curve over all LMs directly without first fitting the separate models (  [12] ). Estimation with the supermodel is based on the LM datasets stacked together to one large dataset. In this "super prediction dataset" the patients are represented as often as they appear in the LM datasets (see  [12] ). A regression model is fitted on the super prediction dataset separately for RR l,h and P E l by accounting for possible time-varying effects at the LMs via interaction terms.
A pseudo-value for D l,h is obtained within the LM dataset at l. For an individual i at risk at LM l the pseudo-observation is defined as θi l,h = n l F1 (l + h|l) -(n l -1) F (-i) 1 (l + h|l), (11) where n l is the number of patients at risk at l, F1 (l + h|l) is the Aalen-Johansen estimator for the conditional cumulative incidence function P (T ≤ l + h, X = 1|T > l), and F (-i) (l + h|l) is the according Aalen-Johansen estimator based on the LM data set at l leaving out observation i. As suggested by  [21]  the pseudo-observations can be easily obtained with the R package pseudo (  [23] ), or alternatively with the R package prodlim (  [24] ). Given the LMs satisfy that the time window (l, l + h] does not exceed the study horizon τ with both the probability of censoring and the probability of still being at risk exceeding zero beyond τ ,  [22]  derive asymptotic properties for the dynamic pseudo-observations.
2. 3. , θi 0l,h is estimated with the patients that are unexposed at LM l. In Section 3. 2. 3. , pseudo-values were estimated with all patients at risk at l. This is because we address two distinct problems. In Section 3. 2. 3. the observable realization of the random variable D l,h was missing due to censoring. Here, we aim to estimate pseudo-values for D 0l,h by assuming that patients who acquire the exposure within the time window (l, l + h] would have the same risk to experience an event as patients who remain unexposed. Thus, patients who acquire the exposure must be treated as censored. After the pseudo-values have been obtained P (D 0l,h = 1|A l = 1) can be also estimated with glms using the R-package geese. The estimate hereby obtained is most likely biased due to a systematic dif-ference between patients who acquire the exposure and patients who remain unexposed.
Therefore, the pseudo-values must be adjusted either via stratification or -more sophisticatedvia nearest-neighbour estimation as proposed by  [27]. However, with increasing length of the time window h, adjustment for timevarying confounding becomes necessary. Therefore, we propose to use adjusted inverse probability of exposure weights as initially proposed by  [28]. The approach is similar to the modified sequential Cox approach proposed by  [29]. The weights are estimated separately for each LM dataset using the R package ipw. Then, based on the LM datasets, two weighted cause-specific Cox regression modelsone for the occurrence of the event of interest within the time window and one for the occurrence of the competing event within the time window -are used to estimate the effect of the exposure within the time window.
This can be done with the R function coxph of the survival package (  [30] ). Patients who remain at risk until l + h are administratively censored at the end of the time window. Finally, P (D 0l,h = 1|A l = 1) can be estimated using the two Cox regression models and the R function survfit. A detailed description of this procedure is provided in the tutorial by  [31]  and the Appendix of  [32]. To obtain appropriate CIs, we propose to use a bootstrap method. Smoothed estimates can be obtained as explained in Section 3.
Data example: Preventable death cases among ventilated patients in intensive care The prevention of hospital-acquired infections caused by multi-drug resistant pathogens is of major interest to public health. In the following, we investigate the OUTCOMEREA French multi-center database. We consider a sample of 7221 invasive-mechanically ventilated (IMV) patients to understand the burden of ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa (V AP P. a. ) on a population level. Patients are considered at risk of V AP P. a. acquisition after two days of IMV. Follow-up was from first day of IMV to death in the ICU (n=1971) or discharge alive from the ICU (n=5250). Followup was complete. However, discharge alive must be considered as competing risk to death in the ICU.
The adjusted versions of P AF LM,h and P AF 0LM,h are shown in the right panel of Figure  7. The estimates reveal that the patient populations are most vulnerable within the first days of IMV (days three to twelve). Here targeted preventive interventions against V AP P. a. with a long term effect of 15 days would provide the most benefit. A plot that contrasts the adjusted versions of P AF LM,h and P AF 0LM,h with the unadjusted ones is provided in the online supplementary material in Figure  S2. Source code to reproduce the results is available as Supporting Information on the journals web page. 7 Data example: TEAM trial data  [14]  studied the risk of death for a sample of the Tamoxifen Exemestane Adjuvant Multinational (TEAM) trial. The randomized clinical trial included Belgian and Dutch early breast cancer (BC) patients that were postmenopausal hormone receptor-positive (HR+) and treated with endocrine.
We obtained unadjusted and adjusted estimates of P AF LM,h and P AF 0LM,h for both exposures, DR and LRR. To demonstrate different ways of estimation, estimation of P AF LM,h was based on definition (3) rather than definition (4) which has been used in the previous data example. The observable overall mortality risk at the LMs within the time window was the same for both estimates, P AF LM,h and P AF 0LM,h , and both exposures. Regarding P AF LM,h , estimation of the death risk among unexposed was based on the patients being unexposed at the LM. Regarding P AF 0LM,h , the estimate was obtained by treating patients experiencing LRR or respectively DR within the time window of five years as administratively censored at the time of recurrence. To obtain adjusted estimates, we adjusted the mortality risk of unexposed patients for each estimand (P AF LM,h and P AF 0LM,h ) with the Cox proportional dynamic prediction model as proposed by  [14].
a. Secondly, we investigated the population-attributable burden of LRR and DR in a population of breast cancer patients. The two data examples served to explain the interpretation of the novel approach. We based our interpretation of P AF LM,h and P AF 0LM,h on two distinct hypothetical interventions. The way the interventions act on exposure may be more or less realistic depending on the data setting. For example, an intervention that prevents LRR or DR at only one specific time point is less realistic than an intervention with a long term effect. In these situations, the estimates can be used to identify high-risk populations and to describe the burden of exposure on a population level. A disadvantage of the proposed estimation procedure is the sample size reduction at the LMs, which leads to a comparatively large variation of P AF 0LM,h and P AF LM,h.
Funding MvC and JFT were supported by the  Innovative Medicines Initiative Joint Undertaking  under grant agreement n [ 115737-2   COMBACTE-MAGNET ], resources of which are composed of financial contribution from the  European Unions Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies; MW has received funding from the  German Research Foundation (Deutsche Forschungsgemeinschaft)  under grant no. WO 1746/1-2.
Inclusion and Exclusion Criteria The data that support the findings of this study are available from the corresponding author on reasonable request. Between 2000 and 2014, patients aged ≥18 years with a diagnosis of at least mild AS (aortic valve calcification with restricted systolic leaflet motion and AVA <2 cm 2 ) and left ventricular ejection fraction (LVEF) ≥50% were prospectively identified and included in an electronic database. The following patients were excluded: (1) individuals with more than mild aortic and mitral regurgitation; (2) patients with prosthetic valves, congenital heart disease (with the exception of bicuspid aortic valves), supravalvular or subvalvular AS, or dynamic left ventricular (LV) outflow tract obstruction; and (3) individuals who declined to participate in the study.
Mortality analyses were performed using Cox proportional hazards models. A predefined Cox proportional hazards model, including covariates with potential prognostic impact (age, sex, BSA, New York Heart Association class, prior AF, mean transaortic pressure gradient, LVEF, history of myocardial infarction, moderate-tosevere aortic valve calcification, Charlson comorbidity index, and AVR treated as a time-dependent variable), was used for multivariate mortality analyses (all data were complete for these variables). Event ratesAESEs were estimated using the Kaplan-Meier method and compared by 2-sided log-rank tests. Each patient with MAS was matched for the average survival (per year) of all patients of the same age and same sex from our region (Somme, department of 555 551 inhabitants, north of France).
Control data were obtained from Somme life tables established on the basis of the 1999 population census, performed by the French Institute of National Statistics, and they represent the survival of the entire Somme general population. 14, 15 Survival rates of patients with MAS were compared with the expected survival of people of the same age and sex in the Somme department. Relative survival was computed as the ratio of observed/expected survival (observed number of deaths in the population with MAS/expected number of deaths in the general population). The limit of statistical significance was P<0. 05, and all tests were 2 tailed. Data were analyzed using SPSS 25. 0 (SPSS Inc, Chicago, IL).
Limitations Because follow-up data were obtained retrospectively, this study, therefore, presents the limitations inherent to this type of analysis. This study exclusively concerned patients with MAS and preserved LVEF without significant valve regurgitation, and results cannot be extrapolated to patients with concomitant LV dysfunction or significant regurgitation. Coexistence of MAS and LV dysfunction is not uncommon and may contribute to worsening myocardial function and symptoms because of increased afterload. Indeed, patients with MAS and LV dysfunction have poor outcome and high risk for clinical events. 24 There is an ongoing randomized trial (TAVR UNLOAD [Transcatheter Aortic Valve Replacement to Unload the Left Ventricle in Patients With Advanced Heart Failure]; NCT02661451) evaluating transcatheter AVR in patients with MAS, heart failure, and LVEF <50%.
Participants After receiving ethical approval from the Northumbria University Faculty of Health and Life Sciences Ethics committee in accordance with the ethical standards established in the Declaration of Helsinki, fifteen male semi-professional soccer players from Level eight of the English Football League, gave written informed consent to participate in the study. Throughout the data collection period, four players sustained injuries which prevented them from completing the study, leaving eleven participants in total (three defenders, five midfielders, three attackers; 22 ± 1 years; stature 1. 80 ± 0. 10 m; mass 78 ± 8 kg). Players trained three to four times a week, in addition to at least one competitive match. The participants competitive season ran from August to May, with testing taking place in the mid-season phase of the players training year.
Design The study employed a randomized cross-over design to assess the effectiveness of PCM on recovery in the days following competitive soccer match-play. Participants visited the laboratory prior to commencement of the data collection period for habituation to the measurement tools employed in the study. For the experimental trials, participants were required to visit the laboratory prior to and 24, 48, and 72 h following two competitive soccer matches. The pre-match visit took place 24 h before the fixtures. On one occasion, players wore shorts fitted with PCM (Glacier Tek; USDA BioPreferred PureTemp, Plymouth, MN) that was either cooled (PCM cold ) or left ambient (PCM amb ), which served as a sham control. The order of the conditions was randomized using an online randomizer 1. Phase change material was applied to the quadriceps and hamstring muscle groups, and was worn for 3 h post-match.
Statistical Analysis Data are presented as mean ± SD. A two-way repeated measures ANOVA with 2 treatment levels (PCM cold vs. PCM amb ) with 4 time points (Pre-, 24, 48, and 72 h post-match) was performed. Normality of the data was assessed using the Shapiro-Wilks test. Assumptions of sphericity were explored and controlled for all variables using the Greenhouse-Geisser adjustment, where necessary. In the event of a significant interaction effect (treatment × time), Bonferonni post hoc analysis was performed to locate where the differences lie. Paired sample t-tests were used to assess differences in match-running and heart rate variables between the two conditions. The belief questionnaire was analyzed using the Wilcoxon signed-rank test. To estimate the magnitude of the treatment effects, Cohen's d effect sizes (ES) were calculated with the magnitude of effects considered either small (0.
Validation of the FACT-BMT QoL tool was conducted using the NIH scores for cGVHD (supplemental Table  2 ). 32 in assessment by TSS was completed by a trained, blinded thirdparty assessor and the trained study investigator. Where possible, subjects were evaluated by the same skin assessor for the duration of the study. Blinding was not used in the LTFU portion of the study. Safety was also evaluated throughout the study. Adverse events (AEs) were categorized by system organ class and preferred term, coded according to the Medical Dictionary for Regulatory Activities (MedDRA), version 16. 0. All AEs were reported after provision of informed consent until study completion at week 28 and were monitored throughout the study by an internal medical designee and the external Data Safety and Monitoring Board.
Continuous data were summarized by treatment group using descriptive statistics (mean, median, standard deviation [SD]). Categorical data were summarized by treatment group using frequency tables (frequencies and percentages). Where the data permitted, 95% CI were estimated for all point estimates. Missing data were not statistically imputed and were treated as failures (no-response); however, last observation carried forward (LOCF) was used to assess the primary endpoint. Patients were included in the efficacy analyses as nonresponders if they dropped out of the study early, could not taper corticosteroid doses according to the study protocol, discontinued due to intolerance to cyclosporine A or tacrolimus, or required additional therapy other  than increased corticosteroid dose and permitted medications for cGVHD treatment.
Safety All 60 patients were evaluated for safety. Overall, 96. 6% of SoC1ECP-treated patients and 90. 3% of SoC-treated patients experienced TEAEs (Table  2 ). The number of events was lower in the SoC1ECP arm (n 5 223) than in the SoC arm (n 5 316). However, because of the small number of subjects in each treatment arm, these data should be interpreted with caution. The most frequently reported TEAEs in the SoC1ECP arm were hypertension (31. 0%), cough (20. 7%), dyspnea (17. 2%), and fatigue (17. 2%), whereas the most common event in the SoC arm was back pain (16. 1%) (Table  2 ). Deep vein thrombosis was not reported by patients treated with SoC1ECP, but was reported by 2 (6. 5%) SoC patients. No cases of pulmonary embolism were reported during this study. Overall, 17 patients experienced 35 SAEs.
7, 26 The FDA has not approved any drugs or devices for first-line therapy of moderate to severe cGVHD. In the present study, the SoC1ECP arm showed an encouraging ORR of 74. 1% at week 28, using an ITT approach, compared with 60. 9% in the SoC arm. Future superiority studies reporting ORR will require larger sample sizes to achieve statistical significance. An ongoing phase 3 study comparing ibrutinib with corticosteroids (#NCT02959944) for first-line therapy of moderate to severe cGVHD is currently underway. If the SoC ORR from the present study is replicated, then it will be necessary for the ibrutinib ORR to be much higher than the 74. 1% achieved in the current study, for a positive result in the first-line setting. Univariate and multivariate analyses from a prospective cohort study showed that treatment responses were more frequent in cGVHD patients who had a platelet count $100 3 10 9 /L (low risk).
Acknowledgments The authors thank the patients and their caregivers in addition to the investigators and their teams who contributed to this study. The authors acknowledge  Gregory Tardie  and  Michael Blasi , both of  Mallinckrodt Pharmaceuticals , for publication coordination. The authors also extend thanks to  Sarah Jayne Clements  ( Costello Medical, Cambridge, United Kingdom ) for medical writing and editorial assistance in the preparation of this manuscript, which was conducted in accordance with Good Publication Practices and funded by  Therakos, Inc , a  Mallinckrodt Pharmaceuticals Company. This study was funded by  Therakos, Inc , a  Mallinckrodt Pharmaceuticals Company.