dataesr/scientific-paragraphs-categorization huggingface dataset:
Using the information available in the different files, the initial database covers the career paths of 18 630 individuals between January 2002 and December 2009, i. e. for seven years. Since our primary interest is temporary contracts, we selected people who had had at least one temporary contract in agricultural production between 1 st January 2002 and 31 st December 2009. We do not consider agricultural workers whose activity is limited exclusively to the grape harvests, and we also eliminate very short contracts which are mainly held by students. In our analysis, an individual's initial condition is considered to be his/her first temporary contract between 1 st January 2002 and 31 st December 2009. In order to avoid difficulties associated with the left-truncated data, we eliminated all contracts that started before 1 st January 2002.
CONCLUSION The main objective of this study was to analyze the role of temporary contracts in agriculture related to the probability of remaining in employment. We also considered the effects of the workers' past career on transitions from the temporary contracts. To do this, we used an original database created by the MSA (Agricultural Social Mutual Fund). In order to consider the heterogeneity of these temporary jobs, we separated seasonal contracts, which have their own specific characteristics, from the non-seasonal temporary contracts. We applied a discrete-time competing risks duration model to both sub-samples, the seasonal contracts and the NSTCs. We first demonstrated the importance of the individual characteristics on the likelihood of exiting the agricultural sector. Whether the contract was seasonal or not, the probability of finding a job outside the agriculture sector was significantly higher for the best qualified workers.
Aims: We investigated the relative pharmacokinetics, pharmacodynamics, and safety of the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor alirocumab following injection at three different sites. Methods: Sixty healthy subjects (39 male, 21 female; age 20-45 years) were randomized to receive a single subcutaneous injection of alirocumab 75 mg via 1-mL prefilled pen into the abdomen, upper arm, or thigh (NCT01785329). Subjects were followed for 85 days AE 2 days following study drug administration. Pharmacokinetic (PK) parameters for the systemic exposure of alirocumab were calculated, and levels of free PCSK9 were assessed. Percentage changes from baseline in LDL-C were compared between injection site groups using linear mixed-effects models. Results: Alirocumab concentrationtime profiles were similar, and free PCSK9 levels were reduced to approximately zero between Day 3 and Day 4 postinjection in all groups.
Here, we report the relative pharmacokinetics (PK), pharmacodynamics (PD), and safety of alirocumab after single subcutaneous (SC) administration of 75 mg into the abdomen, upper arm, and thigh of healthy subjects. (2. 46 mmol/L) not receiving background lipid-lowering therapy. The study was conducted at the Hammersmith Medicines Research Clinical Research Unit in London, UK (NCT01785329). The protocol was approved by the Scotland A Research Ethics Committee, Edinburgh, Scotland, and written informed consent was obtained from all participants. Subjects were randomized to one of the three parallel groups and received a single 75 mg dose of alirocumab SC via 1-mL prefilled pen at one of the three distinct sites (abdomen, upper arm, and thigh) in the morning on Day 1. Samples for PK and PD analyses (including free PCSK9 and LDL-C assessments) were collected following a 10-h fast predose on Day 1, and at various time points up to Day 85 (AE2 days, end of the study).
Statistical Analyses A sample size of 20 subjects per group was calculated to be sufficient to obtain an estimate for the ratio of PK parameters between groups with a maximum imprecision of 19. 7% and 90% assurance in terms of the 90% confidence interval (CI), and assuming a maximum standard deviation (SD) of 0. 35 for log-transformed PK parameters based on previous experience with alirocumab. PK parameters were log-transformed prior to statistical analysis with PKDMS (PKDMS version 2. 0 incorporating WinNonlin Professional, version 5. 2. 1; Pharsight [now Certara, St. Louis, MO, USA]) and SAS â (version 9. 2 on Windows platform; SAS Institute, Cary, NC, USA). Relative PK of systemic exposure between injection sites was assessed using a linear fixed-effects model with terms for injection site, gender, and weight as covariate.
Ratios of geometric means for C max , AUC last , and AUC were obtained by computing estimates and 90% CIs for the differences between injection sites means within the linear mixed-effects model framework, then converting to ratios by antilog transformation. Percentage changes from baseline for each PD parameter were compared between each injection site group at each time point using a linear mixed-effects model (SAS Proc Mixed â ; SAS Institute) to obtain P-values for the interaction effect between injection site and PD parameter. Safety data were analyzed using descriptive statistics.
Data collection interviews Three experienced interviewers (DTE, JSE, and JLR) conducted interviews in the clinics between January 2010 and January 2013. The semistructured interview guide focused on how patients cared for their conditions and the impact that care had on them, including their personal life, social situation, and work. We also asked patients what factors made management of their care easier or more difficult. The interview guide, originally listed in Eton et al,  1  was revised as new issues warranting exploration emerged. Demographic information was collected at the end of each interview.
Data analysis Analysis began with the interview data. All interviews were audio-recorded and transcribed verbatim. Two authors (DTE and DRdO) analyzed data from the Mayo Clinic interviews using framework analysis, a systematic procedure for identifying patterns within narrative data. 24 A coding scheme (ie, "framework") was developed after independent review of the first five interviews, followed by discussion, and consensual agreement of the two analysts. The framework was then applied to subsequent transcripts and revised as needed when new themes and subthemes emerged. Hence, data collection and analysis occurred concurrently. The data from the HCMC interviews were reviewed and coded independently by three members of the research team (DTE, JSE, and JLR), using the coding framework derived from the Mayo Clinic interviews.
The team subsequently met and discussed each transcript, arriving at consensus on each passage of coded text. NVivo qualitative data analysis software (Version 10; QSR International Pty Ltd, Victoria, Australia) was used to facilitate data organization. When existing codes were insufficient, discussion ensued, and consensus was ultimately reached to either alter an existing code or add a new code. Once all interviews were coded, the team performed one last review and modified the conceptual framework accordingly. Analysis of the FG data was tailored to the objectives of the groups, namely, to 1) confirm the framework arising from the interviews and 2) clarify any new (unrepresented) conceptual issues. These objectives lend themselves to a notesbased approach, whereby the data used included field notes and audio files.
25 The facilitator (KT) and three members of the research team (DTE, JSE, and JLR) took detailed notes and held a debriefing session following each group to discuss findings. After the first two groups, top-line reports (preliminary reports produced quickly) were distributed to the clinical members of the study team (DB and ML) for feedback. 25 pon completion of all four FGs, two team members (DTE and JLR) compiled the data into a saturation grid outlining the themes that were identified in each group and refining any themes with overlapping or divergent content. KT and JSE reviewed the report and provided feedback. These data were used to finalize the conceptual framework of treatment burden (manuscript forthcoming). Themes representing potential resources for mitigating treatment burden form the basis of the present analysis.
Acknowledgments The research reported in this publication was supported by the  Sponsorship Research Board of St Marys Hospital (Rochester, MN) ; the  Mayo Clinic Center for Translational Science Activities  through grant number  UL1 RR024150  from the  National Center for Research Resources , a component of the  National Institutes of Health ; and the  National Institute of Nursing Research of the National Institutes of Health  under award number  R21NR012984. DRdO, VMM, and DTE are part of the  International Minimally Disruptive Medicine Workgroup. Workgroup members include  Gene Beyt ,  Amy Bodde ,  Kasey Boehmer ,  Juan Pablo Brito ,  David Eton ,  Katie Gallacher ,  Michael Gionfriddo ,  Leona Han ,  AnneRose Kaiya ,  Maya Kessler ,  Aaron Leppin ,  Sara Macdonald ,  Frances Mair ,  Carl May ,  Victor Montori ,  Megan Morris ,  Djenane Oliveira ,  Nilay Shah ,  Nathan Shippee ,  Amit Sood ,  Robert Stroebel ,  Kristin Vickers Douglas , and  Kathleen Yost.
Treatment for published datasets We have reexamined data from David  [17] , and Werngren & Hoffner  [21]. The data in Table  1  of  [17]  are not detailed, so only the p 0method could be applied. The bias correction (1) was applied, using a coefficient of variation of 0:35 (estimated from Table  2  in the same reference). Table  2  of  [17]  shows 10 pairs mutant counts -final numbers. All possible estimates were computed together with their confidence intervals. However, it must be remarked that standard deviation computations rely upon asymptotic results, and do not apply to such a small sample. In Table  1  of  [21]  mutant counts are explicitly given. The maximum likelihood estimate with exponential division time was computed, then unbiased using a coefficient of variation of 0:44 (estimated from the given final counts).
Background: Chronic kidney disease (CKD) is a progressive and usually irreversible disease. Different types of outcomes are of interest in the course of CKD such as time-to-dialysis, transplantation or decline of the glomerular filtration rate (GFR). Statistical analyses aiming at investigating the association between these outcomes and risk factors raise a number of methodological issues. The objective of this study was to give an overview of these issues and to highlight some statistical methods that can address these topics. Methods: A literature review of statistical methods published between 2002 and 2012 to investigate risk factors of CKD outcomes was conducted within the Scopus database. The results of the review were used to identify important methodological issues as well as to discuss solutions for each type of CKD outcome.
Methods To identify the outcome variables and statistical methods commonly used to investigate the effects of risk factors on progression of CKD, we conducted a literature review restricted to papers written in English language and published from January 2002 to October 1 st , 2012. We used the Scopus database because it covers a wider range of peer-reviewed journals than most other databases  [11]. We performed two searches. Search 1 focused on epidemiological studies (experimental or not) of CKD progression, while Search 2 focused on developments of new statistical methods with application in the field of nephrology. We searched for the same terms in Searches 1 and 2 but we specified different positions of these terms in the papers. Specifically, one of the selected CKD terms (see Table  1 ) had to appear in the title for Search 1; and in the title, key words, or abstract for Search 2.
Regression methods used in the selected papers and related statistical issues The regression methods used for each outcome are reported in the third column of Tables  2  and 3. The last column indicates the occurrence and percentage of the use of the regression method among the papers investigating the specific outcome. The papers that used each of the specific regression models are listed in Table  2  available in the Additional file 1. Below, we describe these methods and discuss potential issues and alternative methods that have been proposed in the context of CKD or in other contexts. Furthermore, Table  4  provides a list of some statistical procedures that are available in SAS, R, or STATA software to perform the most advanced statistical analyses discussed below.
Therefore, the regression coefficients do not have a rate ratio interpretation. However, they are useful for prediction since they have a direct relationship with the cumulative incidence functions which measure the risk (i. e. probability) of an event within a given time interval  [6, 12, 15]. The Fine and Gray model is implemented in several statistical packages (see e. g. the SAS PHSREG macro  [16] ) as indicated in Table  4. Some alternatives to the cause-specific proportional hazards model and to the Fine and Gray model have been proposed and used in the context of CKD. For example, Cianciaruso et al. [17]  used a marginal competing risks model and Scolari et al. [18]  used a frailty model to account for the correlation between ESRD and death times  [19]. However, it should be mentioned that independence of competing risks is not needed for a valid inference of the cause-specific model  [15].
Using an illness-death model for interval-censored data as the one implemented in the SmoothHazard R package  [26]  (Table  4 ) would allow us to account for the fact that patients in the initial state 0 at the last measurement before death may have progressed through the intermediate state 1 of interest before dying. Such a flexibility has been shown to produce accurate estimations of the effects of factors on the state of interest  [24]. However, note that this model requires follow-up information on vital status after transition to the specific stage of interest. When more than one stage is of interest (for example, if one wants to study all stages of CKD) and the followup is long enough to ensure a sufficient number of patients observed at different stages of the disease, even more complex multistate models can be used.
For example, Begun et al. recently considered a six-state model for interval-censored data, where the states were CKD stages 3, 4 and 5, dialysis, transplant, and death  [27]. Foucher et al. proposed four-state and five-state models for the study of kidney transplant evolution  [4, 28] , considering interval-censored data since intermediate states were defined on creatinine clearance and/or proteinuria levels. Hu et al. also proposed a multistate approach to estimate the probabilities to be in CKD stages 1-2, 3 and 4, dialysis, or death before dialysis at different time points, while accounting for the fact that estimated GFR was measured at some specific time points only  [29]. Multistate models are now implemented in several statistical software such as the R package mstate  [30]  (Table  4 ).
O'Hare et al. studied trajectories of GFR using a latent class growth analysis, implemented in the SAS PROC TRAJ  [45]. De Beaudrap et al. also used a latent class growth analysis but with the log of GFR as the outcome to achieve normality  [9]. Briefly, the latent class growth analysis allows identification of classes of individuals following similar progressions of the outcome over time  [46]. However, this method assumes no individual deviation from the class mean trajectory, and independence of repeated measurements of a same patient within the class  [47]. An alternative method which does not have these limitations and has already been used in some contexts other than CKD is the latent class linear mixed model, also called growth mixture modelling  [48] [49] [50]. Although this model is more complex to estimate than the more simple model in the latent class growth analysis, some statistical software packages such as Mplus  [47]  or the lcmm R package, are now available to perform such analyses (Table  4 ).
The lcmm package also handles joint latent class models which may be used to account for informative censoring as discussed below  [51]. Accounting for informative censoring-joint models An important issue in longitudinal analyses of a quantitative marker of the renal function is early study dropout  [52]  due to, e. g. initiation of kidney replacement therapy. If the reason for dropout of the study is unrelated to renal function, the data missing after each individual last observed value of the marker are said to be "missing completely random" (MCAR). In this situation, both standard linear mixed models and standard linear GEE can be used to investigate the effect of risk factors on the mean trajectory of the renal function. If the reason for dropout is associated only with previously observed values of the marker (and not with unobserved values of the renal function after dropout), missing data due to dropout are said to be "missing at random" (MAR).
9%) (see e. g. [55] [56] [57] [58] ). Different statistical approaches have been proposed to jointly model longitudinal quantitative markers and clinical events. They consist of considering a model for the renal function trajectory (usually a linear mixed model) and a model for the time-to-death or kidney replacement therapy (usually a Cox model), and linking both models using a shared latent structure. The most popular joint modelling approach uses a shared randomeffects model. In a methodological paper, Vonesh et al. proposed such an approach to analyse data from the modification of diet in renal diseases (MDRD) study  [58] , and provided some SAS code necessary to implement the method (Table  4 ). Shared random-effects models are also implemented in the JM R package  [59]  (Table  4 ). An alternative approach consists in using a joint latent class model.
As opposed to the first approach, the joint latent class model assumes that the population is divided into various subpopulations with different longitudinal evolutions of the quantitative marker associated with different risk functions for the event  [51]. In the context of CKD, such an approach has been proposed by Garre et al. to jointly model the reciprocal of serum creatinine and time to renal graft failure  [60]. Joint latent class models are implemented in the R package lcmm. It should be noted that the joint modelling approach can also be used for other purposes including accounting for the trajectory of the quantitative marker to dynamically predict the clinical event  [51].
In Social epidemiology, it is very common to examine causes that are distant from the health outcomes investigated, with a distinction between the so-called upstream determinants (e. g. , social class, work economic sectors) and downstream determinants of diseases that mediate the effects of the former. Considering work economic sectors in addition to social class and other broad characteristics of populations may be useful to identify an additional marker of populations in poor health, in order to target interventions to populations who critically need them. The operational interest of work economic sectors for targeting interventions is also related to the fact that this information is commonly reported and available in a number of databases. Finally, work economic sectors are also connected to a potential approach to develop interventions, through the occupational medicine departments represented in each company.
Study population The RECORD Study Data from the first wave of the RECORD (Residential Environment and CORonary heart Disease) Cohort Study (www. record-study. org) were used for cross-sectional analyses. As described elsewhere  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] , 7,290 participants aged 30-79 years were recruited without a priori sampling in 2007-2008 during free preventive medical checkups conducted by the Centre d'Investigations Préventives et Cliniques in the Paris metropolitan area. As an a priori eligibility criterion, only participants residing in 10 (out of 20) administrative districts of Paris or in 111 other municipalities in the region were selected for the study. Of the persons selected for participation, 83. 6% agreed to participate and completed the data collection protocol.
Working participants with information on their workplace Administrative files from CNAV (National Old Age Insurance System) were used to identify the establishment of work. We relied on a database of occupational careers routinely used for the computation of retirement pensions. The file received from CNAV indicated the employer (or employers, with a maximum of 3) of each participant for each year, with the corresponding establishment identification code and work economic sector. The file did not provide information on the dates of beginning and end of the contracts during the year. The data therefore did not allow us to confirm for sure that the participant was employed, nor with which employer he/she was employed (if several employers were reported), at the exact date of enrollment in the study.
The socioeconomic status of the neighborhood was assessed with the educational level of the residents. The variable was computed within buffers with a radius of 1000 m centered on participants' residences. These buffers took into account the street network, i. e. , comprise the area that is accessible within 1000 m along the street network. ArcInfo10 and its Network analyst applied to street network data from the National Geographic Institute were used to derive such buffers.
Work economic sectors and legal category of the establishment Based on the business identification code of each workplace retrieved from the CNAV data, we then used databases of facilities or companies from Insee (Permanent Database of Facilities, SIRENE register) or Trade Dimension to identify the work economic sector of each participant. We used the 17 levels of the 2003 French classification of activities (NAF) to classify work economic sectors. Of those 17 levels of activities, only 11 were represented in our sample: Health and social work; Manufacturing industry; Construction; Commercial, repair of motor vehicles and motorcycles; Hotels and restaurants; Transport and communications; Financial activity; Real estate, renting, and business services; Public administration; Education; and Collective, social and personal services.
Third, the work economic sector is independent of the personal social group of the person, and each work economic sector comprises people of different classes (e. g. , managers and blue-collar workers, low and high white collar workers). Additional analyses were conducted with the work economic sectors combined into a smaller number of groups: the primary sector (extraction and production of raw materials), the secondary sector (transformation of raw or intermediate materials into goods) and the tertiary sector (supply of services to consumers and businesses). The primary sector was excluded from the analyses because of a too small sample size (n = 14 for men and n = 14 for women). The Insee databases also enabled us to distinguish between the private and the public sector.
Statistical analysis Multilevel linear regression models with a random effect at the census tract level were estimated to account for within-neighborhood correlation in the cardiovascular risk factors examined. Given differences in occupation and cardiovascular profiles between females and males, all the analyses were stratified by sex. Models of the relationships between work economic sectors and cardiovascular risk factors were adjusted for individual sociodemographic variables and neighborhood education level. Models for blood pressure were further adjusted for antihypertensive medication use. All the analyses were conducted with SAS 9. 3 (SAS Institute, Cary, North Carolina).