dataesr/scientific-paragraphs-categorization huggingface dataset:
However, participant and researcher burden must be considered; increased days may lead to diminished accuracy of dietary data relative to study fatigue. EFR are a rich source of data that enable the characterisation of eating behaviours. Nutrition intervention strategies frequently solely focus on dietary components, however 'how' we eat may also influence health. Observational studies have characterised breakfast consumption as associated with a lower BMI and may be protective against weight gain  [42, 43]. In a 2019 systematic review and meta-analysis, researchers concluded that while breakfast consumption was associated with increase in total daily calories, there was only a nominal difference in weight between those who consumed breakfast vs. skipped breakfast (respective mean difference 0.
Therefore, replication is required in an independent cohort. This TwinsUK EFR dataset allows for the exploration of the impact of acute diet, and new insights into eating behaviours and co-occurrences of food items on health and disease states in this twin population. However, the predominantly female, older-aged, Caucasian nature of the research cohort should be considered, as may not represent the diversity of the UK population. While the majority of EFR were an acceptable quality, further data improvements may have been possible with the implementation of standard operations procedures for each stage in the data collection and processing pipeline, and are recommended for future dietary data investigations in this cohort. Likewise, the poor portion size estimation should be addressed, whether through provision of a portion size guide or a technology assisted approach such as a mobile application.
Conclusion EFR are affordable, easy to use, non-invasive and flexible by means of implementation (e. g. electronic, paper, online); suitable for large studies. Due to the openended nature of an EFR, EFR are better able to capture the complexities and variability in individual diets in comparison to more structured methods including insight into, for example, eating behaviours and the cooccurrences of food items. This study characterises the quality of the EFR from the TwinsUK cohort as acceptable. However, increased support for participants on reporting portion sizes of food and drink intake may be required, such as through the use of food quantity guides, to facilitate improved consistency in data quality. The impact of breakfast consumption vs. breakfast skipping on health is widely debated in the public arena however this study suggests that other behaviours, such as length of eating window, may be equally aligned to outcomes such as weight and BMI status, requiring further exploration.
Likewise, the moderate heritability of the length of eating window and breakfast consumption are indicative of the need for discrimination and quantification of genetic and environmental influences of eating behaviours, and not just dietary intake. This study offers a preliminary insight into the rich potential of this dataset. The TwinsUK EFR allow for exploration into acute dietary intake, eating behaviours, heritability and co-occurring food choices, and may provide a useful adjunct to habitual diet measures in diet-disease investigations.
Funding The Wellcome Trust, Medical Research Council, European Commission and National Institute for Health Research (NIHR) provide core funding and Support for the Department of Twin Research and Genetic Epidemiology (Twins UK). The research data provided on this paper was specifically funded by the Wellcome trust (079771 and 202786), and the NIHR Biomedical Research Centres (BRC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The objective of the study was to perform a cost-effectiveness analysis of bundled devices (BDs) versus standard devices (SDs) for the prevention of unscheduled peripheral venous catheter (PVC) removal due to complication from a French investigator-initiated, open-label, single center, randomized-controlled, two-by-two factorial trial (CLEAN-3 study). A 14-day time non homogeneous semi-markovian model was performed to be fitted to longitudinal individual patient data from CLEAN-3 database. This model includes five health states and eight transitional events; a base case scenario, two scenario analyses and bootstrap sensitivity analyses were performed. The cost-effectiveness criterion was the cost per patient with unscheduled PVC removal avoided. 989 adult (age�18 years) patients were analyzed to compare the BDs group (494 patients), and the SDs group (495 patients).
This result suggests the use of 2% CHG plus alcohol as the preferred antiseptic for short-term PVC insertion and care. The results for comparison between the two antiseptics were not affected by the type of devices, nor were the results for comparison between standard and innovative devices affected by the type of antiseptic. Moreover, CLEAN-3 clinical study showed that length of stay in hospital were not affected by the choice of antiseptic agent or type of device. As a consequence, two devices groups (combining CHG and PVI solutions) were considered in the cost-effectiveness study: a bundle of innovative devices (BDs), and standard devices (SDs). The objective of the present study was to perform a cost-effectiveness analysis (CEA) of these devices strategies in the prevention of PVC unscheduled removal due to complications in France, from modeling techniques based on the CLEAN-3 database.
Study design Statistical analysis of observed data from CLEAN-3 database was achieved. The adopted modeling approach complies with the guidelines of French National Authority for Health (Haute Autoritede Sante-HAS)  [6]. The 14-day ICU-time non-homogeneous semi-Markovian model structure was based on observed data from the CLEAN-3 study. Modeling and data analyses were performed using Rstudio software (version 1. 1. 453 -© 2009-2018 RStudio, Inc. ). RStudio is an Integrated Development Environment for R (R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www. R-project. org/. ).
Data collection Data of the cost-effectiveness study were from the CLEAN-3  [5]  database delivered by the University Hospital of Poitiers (France). Patients were recruited at the ED before being admitted to the medical wards. The main objective of the clinical study was to hypothesize that skin preparation with 2% chlorhexidine plus 70% isopropanol (chlorhexidine plus alcohol) was more effective than a skin preparation with 5% povidone iodine plus 69% ethanol (povidone iodine plus alcohol) in preventing PVC-related infectious complications. The second assumption testing was to consider that use of closed integrated catheters, positive displacement needleless-connectors, disinfecting caps, and single-use prefilled flush syringes in combination extended the time elapsed between catheter placement and catheter failure compared with the use of open catheters and three-way stopcocks for treatment administration.
Modeling and statistical analysis Markov models simulate the trajectory of patients among distinct health states over time  [7] [8] [9]. The main assumption of state-transition Markov models is that the next health state depends only on the present state and not on the sequence of events that preceded it. For an expected goodness of fit to CLEAN-3 data, a multi-state semi-markovian model in continuous time was performed. This type of modeling can take into account iterative occurrences for each health state (e. g. , Markov state). This modeling is very close to the daily realities that can be observed through the hospital stay (changes in daily health status, costs of care, etc. ). Within this model, transition probabilities between states are time dependent and well suited to individual patient data (IPD) from CLEAN-3 database.
The hospital length of stay (LOS) limited to 28 days was recorded. The type of modeling is a multi-state semi-markovian model in continuous time (transition probabilities are time dependent and fitted to observational IPD from CLEAN-3 database suited to the hospital perspective). For base case scenario, time horizon was of 0-14 days, e. g. , the maximum observed duration of catheterization (hospital length of stay: 0-28 days). The Markov Cycle length was the estimated mean sojourn time in each transient health state, for a given set of covariate values (from observational data in CLEAN-3 database). The number of Markov cycles depends on time horizon. The time horizon was based on the maximum catheter duration observed in each group (SDs group: 336 hours, e. g. , 14 days; BDs group: 216 hours, e.
g. , 9 days) for accounting all types of patients (alive, discharged, or dead). As time horizon observed in CLEAN-3 database was not identical for each group, and for getting a common comparative basis for suited cost-effectiveness analyses, we set the time horizon to 14 days (for base case analysis; all events and catheters were considered from CLEAN-3 database). A scenario analysis considered a time horizon of 2-14 days (subgroup analysis considering patients with a catheter duration of more than 24 hours, or less than 24 hours but with unscheduled catheter removal due to a complication). The statistical unit of the study is the hospitalized patient within a time horizon of 14 days (including patients discharged alive from the hospital, alive but still in the hospital, or deceased during the hospital stay).
Data was censored beyond 14 days as it corresponds to the maximum duration of catheterization observed in CLEAN-3 study. The estimated transition probability matrix is based on individual patient data from CLEAN-3 database. This analysis can be considered as a non-homogeneous Semi-Markov Chain (NH-SMC) analysis which takes into account time dependency of state transition, duration in each health state, and individual path of states through time. The observed transitions among health states are shown on the Markov diagram (Fig  1 ). In order to be as close as possible to observable realities, we have presented the cost-effectiveness results from the matrix of transition probabilities observed by the model (e. g. , 'prevalence. msm' algorithm  [10] ), and not from the matrix of transition probabilities estimated by the model.
This approach is taken by the function prevalence. msm, which constructs a table of observed and expected numbers and percentages of individuals in each state at a set of times. Instead of parametric Monte Carlo simulation, the msm package  [10]  allows to quantify uncertainty with nonparametric bootstrap methods for probabilistic sensitivity analysis and 95% confidence intervals (CI) calculations. To populate the model, data are specified as a series of observations, grouped by patient and sorted by increasing observational time from the patient entry in ED. At minimum there should be a data frame with variables indicating: • The time of the observation, • The observed state of the process, • The subject identification number (ID). Then all the observations are assumed to be from the same subject.
Main assumptions 1. The cost of an event is independent from the outcome (survival or death or discharge): Statistical unit is the "global" patient. "Global" patient indicates a patient who could, during the hospital stay, be alive in the hospital, be alive and discharged of the hospital, or be died; 2. The estimated cost per event at the University Hospital of Poitiers is estimated for each compared intervention; 3. Discharge and Death Markov states are considered as absorbing states, e. g. , patients cannot move from these states. As a consequence, an absorbing state is frequently valued at zero cost, except here considering the step of catheter removal before discharge or death, if a catheter is in place. 4. As no data were available regarding 2 nd catheter follow-up (date of removal), we considered the mean duration of the 2 nd catheter was the difference in days between the censured time horizon (14 days) and the date of second catheter placement.
Base case input parameters for the cost analysis The base case analysis of the cost-effectiveness study has to be the most conservative case as possible, and the most representative case of real life, taking into account current hospital settings in France, and according to clinical experts, literature and RCTs. The patients' characteristics in the CLEAN-3 database were used for modeling (frequency and type of complications leading to catheter replacement, time of occurrence of complications, according to the two study groups). The average cost of catheter insertion, replacement and removal, the average cost of each day of use of the venous line (excluding the cost of treatments administered) and the average cost of treating complications were estimated from the cost of necessary material and nursing time.
Potential additional hospital length of stay due to studied complications. We can estimate the additional LOS due to studied complications based on observational data from CLEAN-3 database through the following calculation: Mean hospital LOS in patients with complications-Mean hospital LOS in patients without complications. A preliminary analysis showed that a mean difference in hospital LOS was of nearly +4 days. This original result will be studied by the University Hospital of Poitiers (UHP). Indeed, considering the studied complications could not clinically induce such an increase in LOS, UHP clinical experts and biostatisticians wish to study if other explanations could be found, as patient characteristics for instance. Therefore, the working group validated the idea for evaluating the intrinsic effect of bundled catheters by considering only the costs related to catheters and studied complications (insertion, duration of use, removal, treatment of studied complications, replacement).
Scenario analysis 2. A NH-SMC analysis (observed results) on "Global" patient sample: Time of observation was of 0-14 1 st PVC-days without PVC complication costs. This scenario removes the cost of complications for which management is not standardized between hospitals, potentially favoring the bundle group. Sensitivity analyses. The estimated transition probability matrix of the NH-SMC model was based on individual patient data from CLEAN-3 study  [5]. This analysis can be considered as a non-homogeneous Semi-Markov Chain Monte Carlo modeling which takes into account time dependency of state transition, duration in each state, and individual path of states through time. Instead of parametric Monte Carlo simulation, the msm package  [10]  allows to quantify uncertainty with nonparametric bootstrap methods.
Results The observed "global" patient (alive, discharge or dead) from CLEAN-3 IPD: Base case scenario Effectiveness: Distribution of patients through health states and transitional events (observed Markov cycle: Each 1. 4 days, a patient can change his/her health status) and duration in each non absorbing health state. The NH-SMC modeling estimated that patients change health states, or remain in the same health state, every 1. 4 days. This Markov cycle is thus close to the observable realities of the Clean-3 database. After 1. 4 days of 1 st catheterization, 58% of patients were still in health state 1 (no event, 1 st PVC) in BDs group, compared to 51% in SDs group. After 2. 8 days of 1 st catheterization, 27% of the patients were still in health state 1 (no event, 1 st PVC in place) in BDs group, against 19% in SDs group.
The non-homogeneous multi-state semi-markovian model (NH-SMC) in continuous time is a suitable mathematical tool to be fitted to longitudinal data based on individual patient data (IPD) available in CLEAN-3 database (Poitiers University Hospital). The literature in this field frequently offers examples based on static decision tree models, used for both cost-effectiveness or cost-benefit studies  [14] [15] [16] , except for the latest Maunoury et al. paper on this topic  [17]. The feature of the current modeling relates to the fact that it is based on real-life individual patient data, and not on published mean values from the literature. The time-dependence addressed here (e. g. , evolution of the risk of developing a PVC-complication with increased catheterization time) corroborates that the non-homogeneous modeling approach is suitable considering the nature of the available data and the medical wards settings.
Acknowledgments The authors would like to thank all stakeholders involved in the CLEAN-3 Study, which is at the origin of this cost-effectiveness study, including the members of the  CLEAN-3 study group :  Emergency Department  and  SAMU 86 Centre 15 ,  University Hospital of Poitiers, France  ( J Guenezan MD ,  N Marjanovic MD ,  B Drugeon MD ,  R O Neill MD ,  Prof O Mimoz PhD );  University of Poitiers ,  UFR de Medecine-Pharmacie, Poitiers, France  ( J Guenezan MD ,  N Marjanovic MD ,  Prof F Roblot PhD ,  P Palazzo PhD ,  M Pichon PhD ,  M Boisson PhD ,  Prof D Frasca PhD ,  Prof O Mimoz PhD );  INSERM U1070, Pharmacology of Anti-Infective Agents, Poitiers, France  ( J Guenezan MD ,  Prof F Roblot PhD ,  M Pichon PhD ,  M Boisson PhD ,  Prof O Mimoz PhD );  Geriatric Department ,  University Hospital of Poitiers, France  ( E Liuu MD );  Department of Infectious and Tropical Diseases, University Hospital of Poitiers, France  ( Prof F Roblot PhD );  Neurology Department, University Hospital of Poitiers, France (P Palazzo PhD); Pneumology Department, University Hospital of Poitiers, France (V Bironneau MD) ;  Hepato-Gastro Enterology Department, University Hospital of Poitiers, France (F Prevost MD), Methodology-Data-Management platform, University Hospital of Poitiers, France  ( J Paul MSc );  Department of Infectious Agents, Bacteriology-Hygiene Laboratory, University Hospital of Poitiers, France  ( M Pichon PhD );  Department of Anesthesia, Intensive Care and Peri-Operative Medicine University Hospital of Poitiers, France  ( M Boisson PhD ,  Prof D Frasca PhD ); INSERM  U1246 , Methods in Patients-centered outcomes and  Health Research-SPHERE, Nantes, France  ( Prof D Frasca PhD ).
51 34. 55 31. 86 1. 6 E6 25. 37-8. 20-2. 32 = 14. 85 28. 33-9. 74-2. 26 = 16. 33 22. 28 21. 23 1. 7 E7 33. 55-8. 20-2. 32 = 23. 03 36. 51-9. 74-2. 26 = 24. 51 34. 55 31. 86 1. 8 E8 24. 95-8. 20-2. 32 = 14. 43 27. 90-9. 74-2. 26 = 15. 91 21. 64 20. 68 3. No Events � / 2 nd 12. 31x n days ��� 13. 27x n days ��� 12. 31x n days x1. 5 13. 27x n days x1. 3 PVC 4. Discharge 2. 32 ���� 2. 26 ���� 3. 47 ���� 2. 94 ���� 5. Death 2. 32 ���� 2. 26 ���� 3. 47 ���� 2. 94 ���� (1) From CLEAN-3 database. PVC: Peripheral venous catheter. �Events:E1, scheduled PVC removal; E2, useless PVC; E3, suspected infection; E4, Dislodgement; E5, phlebitis; E6, diffusion; E7, local infection; E8, occlusion. ��Unitcost for daily use of catheter x Number of catheter-days. ���As no data were available regarding 2nd catheter follow-up (date of ablation), we considered the mean duration of the 2nd catheter was the difference in days between the censured time horizon (14 days) and the date of second catheter placement.
A combination of large empirical genetic datasets and modelled genetic differentiation could therefore be used to delineate conservation units (groupings of a species which contain sufficient biodiversity for persistence through subsequent generations) throughout the entire range of a species. Separating a species' range into conservation units can indeed identify key areas for dispersal along with populations under potential threats  (Allendorf et al. 2010 , Funk et al. 2012 , Barbosa et al. 2018). Although the definition of conservation units has been debated  (Waples and Gaggiotti 2006 , Palsbøll et al. 2007 , Lowe and Allendorf 2010) , the hierarchical delineation of population subdivisions, based on genetic connectivity, can provide significant clues for both local and global management strategies  (Dilts et al.
This species has strongly declined in non-protected reefs close to human habitation  (Robbins et al. 2006 , Juhel et al. 2017 , 2019 , Ruppert et al. 2017)  and is now listed as Endangered on the IUCN Red List. It shows a high level of residency and small home range but adults can perform long-range movements (> 700 km) along reefs and across oceanic waters  (Espinoza et al. 2015a , White et al. 2017 , Bonnin et al. 2019 , 2021). We followed a two-step approach to investigate the genetic connectivity of this near-threatened coral reef-associated predator and delineate hierarchical conservation units based on estimates of genetic connectivity. Firstly, we employed IBR modelling and electrical circuit theory (CT)  (McRae 2006 , McRae et al. 2008)  to determine how seascape features shape the genetic differentiation of this species, using an extensive genetic dataset of over 500 sharks collected in 17 locations across the Indian and Pacific oceans.
Population structure We applied a Bayesian unsupervised clustering method (fast-STRUCTURE) to investigate genetic structure at neutral loci  (Raj et al. 2014). fastSTRUCTURE implements an efficient algorithm for approximate inference of the admixture model from STRUCTURE  (Pritchard et al. 2000). We ran fastSTRUCTURE with simple and logistic priors, at multiple numbers of clusters, K ranging from 1 to 10. We also carried out discriminant analysis of principal components (DAPC) using the R package adegenet, with sampling location of each individual used as prior information  (Jombart et al. 2010)  to investigate patterns of genetic structure at neutral loci. The number of principal components (PCs) to retain for DAPC analyses was determined by cross-validation using a training set of 80% of the data and we therefore retained the number of PCs for which the obtained mean square error was the lowest.
Isolation-by-distance and isolation-by-resistance models The relationship between genetic distance at neutral SNP loci (F ST ) and geographic distance, i. e. isolation-by-distance (IBD), as well as isolation-by-resistance (IBR) patterns, were investigated using multiple regression on distance matrices (MRM; Lichstein 2007; Fig. 2 -step 1 ). First, pairwise genetic distances between all locations (Weir and Cockerham F ST ,  Weir and Cockerham 1984)  were calculated using the R package diveRsity  (Keenan et al. 2013). Pairwise shortest geographic distances by sea between all locations were measured with the R package marmap  (Pante and Simon-Bouhet 2013). IBR was then investigated, to check for further effects of some seascape features (bathymetry, distance-to-habitat) on the dispersal of grey reef shark populations.
Pairwise distances using circuit theory As shown in Fig. 2  -step 1, the set of obtained resistance maps was then used as input for IBR models using GFLOW  (Leonard et al. 2017) , an optimized version of the Circuitscape software, estimating pairwise resistance distances between sampling locations using circuit theory  (McRae 2006, McRae and Beier 2007). This batch of obtained pairwise resistance distances between all locations and for every resistance map was then correlated to pairwise genetic distances (pairwise F ST estimates linearized using the formula F ST /(1 -F ST ); Fig. 2 ). As a result, in addition to the IBD and CT null models, one IBR model was obtained for each resistance map previously obtained. Models were compared and the best model was chosen by looking at a combination of R 2 values from multiple regression matrices (MRMs), Mantel tests  (Mantel 1967)  and AICc (R package AICcmodavg).