dataesr/scientific-paragraphs-categorization huggingface dataset:
The other polynomial parameters, in particular α 0 , are kept fixed to their chirally-matched value. In this way, we obtain at the minimum: χ 2 min = 387, which corresponds to a value per degree of freedom χ 2 min /dof = 1. 055. The fitted values of the polynomial parameters differ from the chirally-matched values by less than 5% (the numerical values are given in appendix D). This confirms that this difference can be consistently attributed to chiral NNLO effects. The theory error in this approach is dominated by the variation (by 10%) of the single polynomial parameter α 0 (we have also added an error associated with the input T -matrices and an error from varying the imaginary parts of the polynomial parameters). The error induced by the variation of the parameters β 0 , γ 0 , β 1 is now computed using the covariance matrix as evaluated by the MINUIT fitting program  [62]  (this matrix is given in appendix D).
D Supplementary material The integral equations for the amplitudes M I depend linearly on the 16 polynomial parameters. The matching equations are also linear. One can then express the amplitudes in which, for instance, the four leading parameters α 0 , β 0 , γ 0 , β 1 are fixed to some arbitrary values and the remaining 12 parameters are fixed from the corresponding matching equations in the form of a linear superposition M I (s) =M We provide our numerical results for the amplitudes in which the parameters α 0 , β 0 , γ 0 , β 1 are either 0 or 1 in five data files: MI 0000. dat, MI 1000. dat, MI 0100. dat, MI 0010. dat, MI 0001. dat. In each file, the first column is s (in GeV 2 ) and the other columns are the corresponding real and imaginary parts of M 0 , M 1 and M 2. We note that these amplitudes depend on the energy value E asy above which the T matrix parameters are set to their asymptotic values.
Acknowledgements B. M. acknowledges rewarding discussions with Émilie Passemar. M. A. acknowledges financial support from the  Spanish MINECO and European FEDER  funds under the contracts  27-13-463B-731  (" Juan de la Cierva" program ),  FIS2014-51948-C2-1-P ,  FIS2014-57026-REDT , and  SEV-2014-0398 , and from  Generalitat Valenciana  under contract  PROM-ETEOII/2014/0068.
Additional analysis After the meta-analysis, meta-regression was used to determine if study characteristics could explain heterogeneity (as measured using the I 2 test). These study characteristics were severity of ID, mean age, and method of data collection (self/carer reported, researcher collected, retrospective records/database). We conducted all analyses using Stata statistical software, version 14 (StataCorp. ). Significance was set at the 5% level (p<0. 05) and 95% confidence intervals (CIs) are presented throughout.
Additional analyses Meta-regression was used to investigate the effects of mean age, severity of ID and data collection method (researcher collected data, self/carer reported data, or retrospective records data) for prevalence of outcomes (Table  7 ). Mean age was found to affect hypertension only (one unit increase in mean age led to 1% increase in prevalence). Severity of ID had no significant effects on prevalence. Data collection method was found to have an effect on any diabetes when database collected data was compared to self/carer reported data (i. e. database studies tended to report lower prevalence of diabetes). It was also found to have an effect on obesity prevalence when database collected data was compared to researcher collected data (i. e. database studies tended to report lower prevalence), see Table  7.
For both methods outlined above, potential participants were approached in the same way as for general practices (easy-read invitation letter, brief information leaflet and reply slip to be returned to the research team). Invitations were crosschecked with those already sent via general practices to avoid duplicate invitations. Non-responders were also followed-up in a similar manner as previously described for general practices; follow-up telephone calls were made by either the custodian of the Learning Disability Register or ID research nurses working on the research programme. A restricted-access database held on a SQL server was used to record and track whether the individual had received the letter of invitation and whether they would like any more information about the project. Capacity assessment and provision of full study information, including involvement of carers and/or consultees, also followed the same process as previously described.
Wrist worn accelerometer The GENEActiv original wrist-worn triaxle waterproof accelerometer (Activinsights Ltd. , Cambridge, UK) was worn continuously on the participants' non-dominant wrist for a minimum of seven days. Data were captured in 100 Hz and processed using two methods. Data analysis method 1: raw acceleration data were converted to 60-s epochs using the GENEActiv Post-Processing PC Software (version 2. 2, GENEActiv; Activinsights Ltd. ). Next, the 60-s epoch data files were entered into an open source Excel macro (v2; Activinsights Ltd. ) in order to classify activity. Subsequently, time spent in sedentary, light-intensity and MVPA activities was calculated for each participant-day using validated cut-points. 230 Sleep time was estimated using a defined algorithm (Activinsights Ltd) and subtracted from total sedentary time, in order to calculate time spent sedentary whilst awake.
Data analysis method 2: Given standard definitions for physical activity categories are lacking for wrist worn devices, we also included an alternative approach reported in the literature using the Euclidian Norm Method. 231 Data were proceeded in a freely available R-package (GGIR version 1. 2-0, http:/cran. r-project. org) using previously described methodology to include time spent in sedentary, light-intensity physical activity and MVPA. 231, 232   n additional, total physical activity levels were reported in mg, where g = gravity.
Baseline data collection Baseline data were collected using the same schedule as in the screening study (work package 1, see Chapter 5). If participants had taken part in the screening stage within the last three months, and valid measurements had been successfully obtained, then these data were used for baseline values. If a participant took part in the screening stage >3-months ago, then the measurements were repeated. Data collected included: • weight • height • BMI • waist circumference • BP • dietary intake (fruit, vegetables and salad). Physical activity (time spent in light, moderate, vigorous activity) and sedentary behaviour (time spent sedentary) were measured using the wrist worn accelerometer (GENEActiv, Activinsights Ltd. , Cambridge, UK). The process for measuring activity using this accelerometer has previously been described in Chapter 7.
Discussion Overall, the findings suggest that it is feasible to collect outcome measures at two time points, before and 3-months after attendance at a lifestyle behaviour modification intervention. Twenty-six per cent (n=5) of people who were invited to take part in the feasibility phase agreed to participate. At baseline, anthropometric measures and BP were obtained for all participants, and accelerometer data for 80%. Attendance at the education programme was overall good, with 80% of participants attending ≥5 days (out of seven sessions for the main programme). At 3-months follow-up repeat data were successfully collected for a high proportion of participants (anthropometric measures 100%; BP 80%; accelerometer data 60%). It is acknowledged that the feasibility phase only involved a very small number of participants (n=5) and carers.
Review of Relationship between physical activity (steps) and risk factors for cardiovascular disease and diabetes in the model To identify a suitable mapping between steps and model risk factors, a targeted review of the published literature was carried out using a search of the on-line PubMed database, a subsequent citation search, and advice from clinicians. This evolved into a 3-step process as described below. Step 1: Search strategy for physical activity studies reporting step as an outcome The search strategy using the PubMed publications database is detailed in Appendix 23 (Table  66 ). The search yielded 153 results, from which 54 abstracts were selected based on titles. Of these, 19 were relevant, of which full text was available for 14. One of these studies, Stuckey 2011,  270  discussed a meta-analysis undertaken by Bravata and colleagues,  271  which is discussed below.
General practitioner attendance and opportunistic screening Frequency of GP visits (separate from NHS health checks) was simulated in the dataset for two reasons; first, to estimate the healthcare utilisation for the ID population without diabetes and CVD and secondly, to predict the likelihood that individuals participate in opportunistic screening for diabetes and elevated risk factors for CVD. This is important as many individuals in the model cannot be diagnosed through annual health checks, either due to ineligibility or because they do not consent to blood tests as part of their Learning Disability Health Check. It was assumed that GP attendance in the ID population occurs at the same frequency as in the general population. However, for cost purposes, consultations were assumed to take 40% longer than the general population average (see Costs section).
The rate of other cause mortality by age and sex was treated as the baseline hazard. An increased risk of mortality was assigned to individuals with diabetes using data from a published meta-analysis. 285 This study used data from 820,900 people from 97 prospective studies to calculate hazard ratios for cause-specific death, according to baseline diabetes status. 285 Cause of death was separated into vascular disease, cancer and other cause mortality. From this study it was estimated that individuals with a diagnosis of diabetes have a fixed increased risk of other cause mortality (Hazard ratio 1. 8 (95% CI 1. 71-1. 9)). The estimates reported in the meta-analysis include increased risk of death from renal disease; therefore mortality from renal disease was not simulated separately to avoid double counting of benefits.
Cardiovascular Disease The QRISK2 model was chosen to estimate cardiovascular risk and incidence as it is a validated model based on a UK population. 286   67  and Table  68 ). The QRISK2 assumptions regarding the relationship between diabetes and CVD were modified to reflect observations from the UKPDS and the European Prospective Investigation into Cancer and Nutrition (EPIC) that HbA1c (rather than diabetes) increases the risk of MI and Stroke in a linear manner. 277, 287    STOP Diabetes baseline data had no information about atrial fibrillation; however it did have a category representing unspecified other heart conditions, which was recorded for 10 individuals (1. 6% of total population). A diagnosis of atrial fibrillation was randomly assigned to 7 of these individuals in line with an audit of patients with ID in North Essex, which found that 1.
Osteoarthritis Osteoarthritis is related to BMI and diabetes, and so was included in the SPHR model. It was assumed that risk of osteoarthritis would be the same in an ID population as in the general population, taking into account individual differences in risk factors. The Bruneck cohort, a longitudinal study of inhabitants of a town in Italy reported diabetes and BMI as independent risk factors for osteoarthritis. 300 The data used to estimate the incidence of osteoarthritis is reported in Table  49.
Body mass index and utility It was assumed that changes in BMI will impact on the utility of an individual with ID in the same way as for an individual in the general population. In a previous modelling of diabetes prevention, weight loss from education interventions was associated with an increase in utility of 0. 0025 per kg change in weight. 302 This estimate was derived from weight loss trial data in which all participants were overweight or obese. In the ID population a large proportion of individuals are normal or underweight so it would not be appropriate to extrapolate the effects of weight loss on utility to these individuals. A change in utility due to a change in BMI was added to an individual's EQ-5D if they had a BMI greater than 25. As a consequence, obese individuals who reduce their BMI as a result of the intervention will experience an increase in EQ-5D.
303 e to the number of health states it was not practical to conduct a systematic review to identify utility decrements for all health states. Furthermore, there is very little or no data to inform utility decrements for comorbid conditions specifically in individuals with ID. A pragmatic approach was taken to search for health states within existing HTA for the relevant disease area in the general population or by considering studies used in previous economic models for diabetes prevention. Discussions with experts in health economic modelling were also used to identify prominent sources of data for health state utilities. Two sources of data were identified for diabetes related complications. A recent study from the UKPDS estimated the impact of changes in health states from a longitudinal cohort.
304 This data was used to estimate the utility decrement for amputation and congestive heart failure. The absolute decrement for amputation was converted into utility decrement factors that could be multiplied by the individuals' current EQ-5D to estimate the relative effect of the complication. Utility decrements for renal failure and foot ulcers were not available from the UKPDS study described above, so were obtained from a different study of 2,048 subjects with T1DM and T2DM. 305 ility decrements for the variety of cardiovascular events were taken from an HTA assessing statins to reflect the utility decrements in the general population. 289 A burden of illness study with a broad utility decrement for cancer was identified and used to define utility decrements for breast and colon cancer.
Unanswered questions and further research Further subgroup analyses could be undertaken in groups at 10%, 15% or 20% 10year risk of CVD (diabetes risk could also be factored in), although such sub-groups would result in a very small proportion of the STOP Diabetes cohort actually receiving the intervention. Further research is needed to identify the optimal mix of initial and maintenance sessions for physical activity programs, together with a better understanding of how long benefits are likely to last. Modelling may also help to inform primary research worn device, compared with 62% (47 of 76 approached) of those consenting to wear the wrist-worn device. Similarly, valid data were obtained from 57% (n=55) of the sample who wore the waist-worn devices, compared with 83% (n=39) of those wearing the wrist-worn devices.
Validation of the Leicester self-assessment diabetes risk score in people with intellectual disabilities When the seven risk factors in the Leicester Self-Assessment risk score were used to explore risk of having undiagnosed IGR/T2DM among people with ID (with data available), the risk score achieved a sensitivity of 82% in identifying those with abnormal glucose regulation. High sensitivity is generally considered most important for screening tools because the priority is to 'rule out' the disease without missing true cases. Ninety-eight per cent of participants with a low/medium risk score were correctly identified as being at low risk. Our findings suggest that the Leicester Self-Assessment risk score is statistically effective at identifying people with ID who are at risk of undiagnosed IGR/T2DM and does not require modification, if it was integrated at practice level.
We aim to explore other ways in which the intervention may be adapted to minimise resources, such as the potential to target carers under certain circumstances. Finally, the findings also revealed a lack of good quality evidence for the durability of effects of multi-component behaviour change interventions, such as that developed for the STOP diabetes programme. circumference, BP and dietary intake (portions of fruit and vegetables), and objective measures for physical activity and sedentary behaviour, using wrist-worn accelerometers, both before and after (3-months) attending the programme. At baseline, anthropometric measures and BP were obtained for all participants, and accelerometer data for 80%. Attendance at the education programme was overall good, with 80% of participants attending ≥5 days (out of seven sessions for the main programme).
At 3-months follow-up repeat data were successfully collected for a high proportion of participants (anthropometric measures 100%; BP 80%; accelerometer data 60%). Owing to time restrictions, we were only able to conduct one feasibility cycle and were also unable to assess whether it is possible to collect longer term data, but these preliminary findings are overall positive. Only four of the five participants who took part in the intervention agreed to wear the wrist-worn accelerometers at baseline, and this suggests that an assessment of willingness to wear the accelerometer is an important component of any future evaluation work. Furthermore, the feasibility component of our work suggested that lifestyle circumstances could play an important role in adhering to the education programme and this needs to be considered for future work.
is based upon individual longitudinal trajectories of metabolic risk factors (BMI, latent blood glucose, total cholesterol, HDL cholesterol and systolic BP), derived from statistical modelling of the dataset from the Whitehall II cohort study. 279 The statistical modelling uses parallel latent growth modelling to incorporate correlations and associations between risk factors that impact upon long term risk profiles. An advantage of the parallel growth analysis is that it is possible to estimate the effect of growth in BMI on the other metabolic risk factors so, for example, a change in an individual's BMI will result in an indirect change in their HbA1c trajectory. Growth factors are also conditional on several individual characteristics including age, sex, ethnicity, smoking, family history of CVD, and family history of type 2 diabetes.
ACKNOWLEDGEMENTS This project was funded by the  NIHR Programme Grants for Applied Research  and will be published in full in Programme Grants for Applied Research; Vol. X, No. X. See the ( PGfAR programme website ) for further project information. The work by TC was supported by a PhD studentship funded jointly by the  University of Leicester and Leicestershire Partnership NHS Trust. The authors gratefully acknowledge the people with ID, their families and health professionals who took part in the STOP diabetes study. We would especially like to thank the contribution from service users and facilitators from the ' Speaking up for Health' and the 'Charnwood Action ' self-advocacy groups who helped us throughout the research process. We acknowledge help from key people throughout the STOP diabetes research programme:  Colin Greaves  for his excellent chairing of the steering group meetings; research nurses,  Ella Bailey ,  Clare Makepeace ,  Kay Massey ,  Elaine Perkins  and  Paul Underwood ; administrative expertise from Yvette Walters; educational programme support from  Marian Carey , Director of the  DESMOND  programme and support from  Lesley Green  ( Leicestershire Learning Disability Register ).
70 % mild/moderate ID 11 0. 001 (0. 00, 0. 00) 0. 23 Obesity Database vs self/carer reported data 35 -0. 05 (-0. 19, 0. 08) 0. 44 Database vs researcher collected data 35 -0. 12 (-0. 21, -0. 02) 0. 02* Mean age 18 -0. 0009 (-0. 01, 0. 01) 0. 77 % mild/moderate ID 17 0. 002 (0. 00, 0. 01) 0. 07 Overweight Database vs self/carer reported data 32 0. 06 (-0. 05, 0. 17) 0. 26 Database vs researcher collected data 32 0. 01 (-0. 07, 0. 1) 0. 8 Mean age 17 0. 01 (0. 00, 0. 01) 0. 16 % mild/moderate ID 16 0. 002 (-0. 00, 0. 01) 0. 27 Hypertension Database vs self/carer reported data 28 -0. 08 (-0. 21, 0. 04) 0. 17 Database vs researcher collected data 28 -0. 01 (-0. 12, 0. 11) 0. 89 Mean age 13 0. 01 (0. 00, 0. 02) 0. 05* % mild/moderate ID 13 0. 0008 (-0. 01, 0. 01) 0. 74.
Background Real-world studies on anticoagulants are mostly performed on health insurance databases, limited to reported events, and sometimes far from every-day issues in family practice. We assess the presence of data for safe monitoring of oral anticoagulants in general practice, and compare patients' knowledge of taking an anticoagulant between vitamin K antagonists (VKA) and direct anticoagulants (DOAC), and the general practitioner's perception of their adherence to anticoagulation.
However, these studies are mostly performed on health insurance databases, limited to reported events. Other practical issues on anticoagulant management by general practitioners remain. Having available renal and hepatic function tests (in order to adjust drug and regimen choices), performing coagulation tests when appropriate, informing patient, inquiring about adherence and potential interactions are all tasks assigned to the general practitioner, as well as all potential sources of ADR  [10, 11]. The primary aim of this study was to assess in general practice records the presence of mandatory data for safe monitoring of oral anticoagulants. The secondary objectives were to compare between vitamin K antagonists (VKA) and DOAC, patient knowledge of taking an anticoagulant, and the perception by the general practitioners of their adherence to anticoagulation.
Materials and methods The CACAO study (Comparison of Accidents and Circumstances with Oral Anticoagulants) is a national cohort study, conducted by general practitioners from all over France on ambulatory patients with an oral anticoagulant. These 463 investigators covered 290 different rural or urban towns, and were distributed over 47 different departments of France. This study was approved by the ethical committee of the University Hospital of Saint-Etienne (IRBN112014/ CHUSTE). Its protocol has been registered in ClinicalTrials. gov (NCT02376777). All patients received written information about the study, emphasizing their right to refuse participation, or to withdraw at any time. No written informed consent was required for inclusion. This cohort is divided into two distinct periods: phase 1 (baseline data) and phase 2 (follow-up data).
Data collection When the patient was eligible, the general practitioner first asked a standardized question, using the following formulation: "Can you tell me if, among your medications, there is an anticoagulant?". Then the general practitioner collected demographic status, personal history, current medications, items of CHA2DS2-Vasc and HAS-BLED scores  [12, 13]  and results of available biological tests performed (renal and hepatic functions, coagulation tests). Co-medication list was built from the ANSM (National agency for medication safety) list  [4]. No specific biological sample was asked for this study, as our aim was to assess the prevalence of information available from the patient's medical record. The general practitioner was finally asked to rate his perception of the patient's adherence to anticoagulant.
Statistical analysis We used descriptive statistics with a 95% confidence interval (CI) to analyze the prevalence of available information, patients' knowledge and their adherence to treatment as perceived by the general practitioners. Normality tests have been performed with quantitative variables. In case of normality, these variables were described by their mean and standard deviation. In other cases, they were described by their median and interquartile range, Q1-Q3. Data were secondarily compared between VKA and DOAC groups, using Chi-squared or Fisher's exact test for qualitative data, and Student's t-tests or rank tests for quantitative data. We performed analysis using IBM SPSS Statistics 201 (International Business Machines Corp).