dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the  Fishing Activity Service of the Cantabrian Government  for the authorization to collect modern Phorcus lineatus specimens and the  Aquaculture Facility of Santander's Oceanographic Centre  for providing seawater temperature information. We also thank the  Universidad de Cantabria (UC) ,  Universidad Complutense de Madrid (UCM) ,  Instituto Internacional de Investigaciones Prehistóricas de Cantabria (IIIPC) ,  Grupo de Ingeniería Fotónica (GIF)  and the  Max Plank Institute for Science of Human History (MPG)  for providing support. We would also like to thank  José Miguel López-Higuera  (GIF) as group leader and  Lucía Agudo Pérez  (IIIPC) and  Patricia Bello Alonso (CENIEH)  for their help during the experimental programme. We also thank the two anonymous reviewers for their useful comments.
Funding information Open access funding provided by  Max Planck Society. This research was performed as part of the projects  HAR2016-75605-R  and  HAR2017-86262-P , funded by the  Spanish Ministry of Economy and Competitiveness, MINECO. During the development of this research, AGE was funded by the  University of Cantabria  through a  pre-doctoral grant  (no code available) and is currently supported by the  Basque Country Postdoctoral Programme  (grant number  POS_2018_1_ 0016 ). DCS was supported by the  Juan de la Cierva Research Programme  ( IJCI-2014-20590 ) of the  Spanish Ministry of Economy and Competitiveness.
Round 2 and subsequent rounds: Prior to Round 2, panel members received their scores from Round 1 alongside deidentified scores of the other panel members (as raw data and as summary bar charts), and the calculated median appropriateness and DI values. Information on interpretation of median appropriateness and DI values was provided. In addition, median appropriateness values were calculated separately for panel members who were anaesthetists and those who were either allergists or immunologists and these values were also circulated to panel members. In generating the statements for Round 2, the writing group reviewed the responses to the statements in Round 1, including the freehand comments, and agreed whether each statement should be included in Round 2 unchanged, included in amended form or not included in Round 2.
PMH: is an Editorial Board Member of BJA. PMM: Scientific advisor for the ALPHO study (NCT02250729), funded by a consortium of pharmaceutical companies: Zambon, Urgo, Pierre Fabre, Boots, Hepatoum, Biocodex, Sanofi, LBR, GSK, APL, Bells Healthcare, Pinewood, T & R, Ernest Jackson. PK has received lectures fees from Novartis Pharma Services Inc, Shire Pharmaceuticals Group Plc SM is in receipt of an Australian National Health and Medical Research Council (NHMRC) ECR Fellowship for the investigation of cognitive aids in emergencies.
Introduction: This retrospective, observational cohort study evaluated the effect of therapy intensification on change in glycated hemoglobin (HbA1c) at 6 and 12 months post intensification in patients with type 2 diabetes (T2D) suboptimally controlled on basal insulin (BI) (i. e. , HbA1c C 7. 5% [C 58 mmol/mol]). Methods: Patients with T2D with suboptimal glycemic control using BI were identified from The Health Improvement Network (THIN) database. Patients who underwent therapy intensification (intensifiers) within 12 months of index 1 (the date of the first incidence of suboptimally controlled HbA1c) were matched (1:1) to patients who did not intensify therapy (non-intensifiers). Index 2 was the date of therapy intensification for intensifiers, or a pseudo date for non-intensifiers that resulted in the same duration from index 1 to index 2 as their matched intensifier patient.
Study Design and Data Source The Health Improvement Network (THIN) database was used in this retrospective observational cohort analysis. This is a UK primary care database of pseudonymized electronic medical records from 3. 1 million actively registered patients in 390 general practitioner practices as of January 2017. THIN was considered to be a suitable data source for this study as T2D is managed in the primary care setting in the UK  [7] , and the prevalence of T2D in this database is comparable with national estimates for the UK  [14]. The study protocol was approved by the THIN Scientific Review Committee under submission 17THIN068.
Study Cohorts Patients aged C 18 years with T2D and suboptimal glycemic control who had been prescribed BI between 1 January 2005 and 16 May 2017 were identified from the THIN database. The date of the first incidence of suboptimally controlled HbA1c was recorded as index 1 for each patient. For patients who intensified therapy (termed 'intensifiers'), the date of therapy intensification was recorded as index 2. A control cohort of patients with suboptimally controlled HbA1c who did not intensify therapy (termed 'non-intensifiers') were matched on a 1:1 basis with intensifiers. Exact matching was performed between the two groups according to: age group at baseline (as per Table  1 ); sex; baseline HbA1c category (7-7. 9, 8-8. 9, 9-9. 9, 10-10. 9, 11-11. 9, C 12% [53-63, 64-74, 75-85, 86-96, 97-107, C 108 mmol/mol, respectively]); number of unique prescribed antihyperglycemia drugs at index 1 (1, 2, or C 3); and time from first T2D diagnosis to index 1.
The formula defined by Basak et al. [15]  was used to account for overlapping combinations of prescriptions. Adherence to intensification therapy post index 2 was measured by the PDC for the intensification therapy during a 365-day follow-up period and included the use of any drug qualifying as therapy intensification at index 2. The PDC is commonly used to measure insulin adherence in database studies  [16]. In about 63% of patients, the insulin dose was derived from THIN and, in about 37% of patients, it was imputed based on data available. Adherence post index 2 was not measured for BMI body mass index, GLP-1 RA glucagon-like peptide-1 receptor agonist, HbA1c glycated hemoglobin, RAI rapid-acting insulin, SD standard deviation a Indicates patient numbers \ 6 for whom data were not provided in order to maintain anonymity the non-intensifiers as they did not receive an intensification therapy.
As pharmacy claims data were not available, persistence was defined as a period free of any gap in therapy of [ 60 days post index 2 (gaps of B 60 days were allowed). Gaps in treatment were calculated based on the time between the calculated end date for a prescription (based on start date and recorded or derived duration) and the start date of the next prescription. If multiple persistent periods were observed post index 2, the first period was used. Delay in therapy intensification was measured in days from index 1 to index 2 for patients who intensified at any point during the study (i. e. , all intensifiers, plus a subset of nonintensifiers who intensified therapy later than 12 months post index 1).
Baseline Characteristics We identified 1342 BI users (n = 646 intensifiers, n = 696 non-intensifiers) from the THIN database who met the study inclusion criteria (Fig. 2 ). Baseline characteristics are shown in Table  1. Mean baseline HbA1c was similar in the two groups: 9. 38% (standard deviation [SD] 1. 45] and 9. 40% (SD 1. 57), respectively. Overall, 90% of patients were assigned a target HbA1c of 7. 5% at baseline, with the reminder being assigned a target HbA1c of 8. 0%. Among the intensifiers, 91. 5% added a RAI and 8. 5% added a GLP-1 RA to their BI therapy. Mean weight, body mass index (BMI), and prevalence of obesity were all higher in the subgroup of intensifiers who added a GLP-1 RA than in the subgroup of non-intensifiers. This observation is consistent with NICE guideline recommendations that adults with T2D who have a BMI C 35 kg/m 2 should receive a GLP-1 RA  [7].
These findings are consistent with those reported in other studies conducted in both the USA and Europe. In a study of a large primary care-based population (n & 12,000) in the UK, only 31% of eligible patients with T2D and HbA1c suboptimally controlled with BI (HbA1c C 7. 5% [C 58 mmol/mol]) underwent therapy intensification during the 9-year study period, with a median time to intensification of 3. 7 years  [10]. Similar findings were reported by an earlier retrospective analysis of the THIN database that included both patients with controlled and suboptimally controlled T2D who (1) were current BI users and (2) who received a prescription for BI during the period January to June 2006  [11]. During a mean follow-up of 2. 9 years, only 19. 3% of patients intensified therapy, whereas 59. 8% remained on their initial BI regimen during the entire follow-up period, despite evidence of poor glycemic control (mean HbA1c 8.
4% [69 mmol/mol] at baseline and 8. 1%  Patients who underwent intensification had a higher mean HbA1c at baseline (9. 2% [77 mmol/mol]) but had an HbA1c decrease of 0. 6% from baseline to end of follow-up. In the present study, early reductions in HbA1c were noted in the non-intensifier cohort that were similar to those seen in the intensifier cohort, but only for the first 3 months. This may have been due to dose adjustments and insulin titration, but data on these treatments were not captured in the current study. Importantly, the intensifier cohort continued to show constant and sustained improvement for the period of observation until 12 months. This HbA1c change and target attainment represent a clinically relevant advantage for patients who intensify therapy when contextualized with the results of the UK Prospective Diabetes Study (UKPDS) study  [4] , which demonstrated that each 1% reduction in HbA1c corresponded with a 21% risk reduction in diabetes-related deaths, a 14% risk reduction for myocardial infarctions, and a 37% risk reduction for microvascular complications  [4].
A review of a retrospective database study in the USA  [23]  found that patients adding a RAI to their current therapy remained persistent for only 274 days, approximately half the time observed in the present analysis. Additionally, in a further study in the USA, for those using a GLP-1 RA and BI, median persistence was 133 days  [24] , which is less than a quarter of the persistence with therapy observed in our study. However, comparisons between European and American persistence data are complicated by access issues around private health insurance and co-payments, differences in electronic health record data capture, and routine health care practices (among other factors). There are some key limitations to this study. Differences in disease severity may affect comparisons between intensifiers and non-intensifiers, although attempts were made to control for such known confounding variables through matching of patients according to baseline characteristics.
Sanofi. Alka Shaunik is an employee of Sanofi. Elisheva Lew is an employee of Sanofi. Compliance with Ethics Guidelines. The study protocol was approved by the THIN Scientific Review Committee under submission 17THIN068. Data Availability. The datasets generated during and/or analyzed during the current study are not publicly available due to patient privacy but are available from the corresponding author on reasonable request. THIN is a registered trademark of Cegedim SA in the UK and other countries. Reference made to the THIN database is intended to be descriptive of the data asset licensed by IQVIA. This work uses de-identified data provided by patients as a part of their routine primary care. Open Access. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.
ACKNOWLEDGEMENTS Funding. This study and the Rapid Service Fees were funded by  Sanofi. All authors had access to all of the data report and output in this study and take complete responsibility for the integrity of the data and accuracy of the data analysis. Medical Writing, Editorial, and  Other Assistance. The authors received writing/editorial support in the preparation of this article provided by  Yunyu Huang, PhD , of  Excerpta Medica , funded by  Sanofi.
Abstract. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Operations (CALIPSO) mission released version 4. 1 (V4) of their lidar level 2 cloud and aerosol data products in November 2016. These new products were derived from the CALIPSO V4 lidar level 1 data, in which the calibration of the measured backscatter data at both 532 and 1064 nm was significantly improved. This paper describes updates to the V4 level 2 cloud-aerosol discrimination (CAD) algorithm that more accurately differentiate between clouds and aerosols throughout the Earth's atmosphere. The level 2 data products are improved with new CAD probability density functions (PDFs) that were developed to accommodate extensive calibration changes in the level 1 data. To enable more reliable identification of aerosol layers lofted into the upper troposphere and lower stratosphere, the CAD training dataset used in the earlier data releases was expanded to include stratospheric layers and representative examples of volcanic aerosol layers.
Building V4 PDFs The V3 CAD algorithm is based on the following confidence function  (Liu et al. , 2010) : f = (1) P c β 532 , χ , δ v , z mid , lat -P a β 532 , χ , δ v , z mid , lat P c β 532 , χ , δ v , z mid , lat + P a β 532 , χ , δ v , z mid , lat. In this equation, P c and P a are the 5-D PDFs for cloud and aerosol, respectively. z mid is the mid-layer altitude, and lat is the layer latitude. The function f is a normalized differential probability that ranges from -1 to 1. The CAD score reported in the CALIOP level 2 products converts f to a percentile (integer) ranging from -100 to 100. A feature is classified as cloud when f ≥ 0 and as aerosol when f < 0. The absolute value of the CAD score provides a confidence level for the classification. In the construction of V4 PDFs, the training dataset for a given altitude range (0-1, 1-2,.
A s , β 532 s,0 , χ s,0 , σ 2 ln β 532 s , and σ 2 χ s can be determined in the grids in which there is only one feature type (i. e. , aerosol, water cloud, or ice cloud) or where there are multiple types of features that separate well (such as the δ v > 6 % cases shown in Fig. 2 ). Interpolation and/or extrapolation are then used to determine these parameters for the grids in which either the clusters do not separate well or there are not sufficient data for some feature types. After using the training dataset to determine a global set of these PDF parameters, the V4 PDFs are interpolated to a uniform size of 1 km for the altitude dimension from the surface to 18 km and 5 • for the latitude dimension from 90 • S to 90 • N. Above 18 km, the PDFs retain their latitude dependence but are no longer altitude dependent.
This technique helps fill the grid cells in which there are either no data or insufficient data for PDF construction. 2008 ) and V3 PDFs for the same latitude band and altitude range as in Fig. 4. We note that there are almost no useful samples for small δ v values (< 10 %) in either the V3 or V4 training datasets. Compared with the V3 training dataset, in which only one distribution mode is seen (Fig. 5 ), the V4 training dataset has two distribution modes for large δ v values (Fig. 4e-j ). By comparing the 2008 and June 2011 periods, we find that the upper mode corresponds to ice clouds, as seen in the V3 training dataset, and the lower mode corresponds to volcanic ash aerosol from the June 2011 Puyehue-Cordón volcanic eruption in southern Chile. The June 2011 data thus provide the stratospheric aerosol properties needed for comprehensive PDF generation.
Because these features are, by definition, always found adjacent to cirrus clouds, they are referred to as "cirrus fringes", which are a new feature in the V4 dataset. As described earlier, 1 month of volcanic aerosol data were added to the 1-year training dataset. These additional training data helped better constrain the characteristic PDF parameters (especially β 532 0 and χ 0 ) at high altitudes, and the new V4 aerosol PDFs are now more sensitive to lofted depolarizing aerosols at high altitudes. As illustrated in detail in Sect. 5, this increased sensitivity helps better differentiate lofted aerosol layers from clouds. At the same time, however, there is a cost to be paid for this increase in sensitivity, as the V4 CAD algorithm preferentially classifies cirrus fringes as depolarizing aerosols, not clouds.
To rectify these perceived misclassifications by the CAD algorithm, a "cirrus fringe amelioration" algorithm has been developed and added as a V4 CAD post-processor. To be identified as a cirrus fringe, a layer must (a) be initially classified by the CAD algorithm as an aerosol, (b) be detected at a 20 or 80 km horizontal averaging resolution, (c) be in direct contact with one or more layers detected at finer resolution and classified as cirrus, (d) have an attenuated backscatter centroid temperature below 0 • C, and (e) have a base altitude higher than 4 km above ground level. Layers that meet all of these criteria are classified as cirrus fringes and given a special CAD score of 106. Application of these simple criteria shows that fringes are ubiquitous within the V4 dataset. For the data acquired between 60 • N and 60 • S during 2014, 22 % of all unique layers detected at 20 and 80 km averaging resolutions with bases between 4 and 16 km are identified as cirrus fringes.
Because the lower 532 nm calibration coefficients in V4 increase the magnitude of the attenuated backscatter coefficients, the V4 feature finder detection totals increased by 12. 6 % and 6. 2 %, respectively, at night and during the day (refer to Table A1 in Appendix A). At the same time, about 3. 6 % of cloud and aerosol features reported in V3 during both night and day were not detected in V4, resulting in a net increase of 9. 0 % in the V4 nighttime data and 2. 5 % in the daytime data. The new features that are excluded in Table  3  are classified about equally as clouds and aerosols in V4.
Stratospheric volcanic aerosol The CAD algorithm was originally designed for cloud and aerosol layers in the troposphere and in previous versions did not include data from the higher altitudes in its training set. As such, layers detected in the stratosphere were not characterized in versions prior to V4 and were instead simply called stratospheric features. The training set used to develop the V4 CAD PDFs included stratospheric data from all of 2008 and from two volcanic eruptions in June 2011 (see Sect. 3). These PDFs were subsequently applied to all layers detected in the stratosphere in the V4 operational algorithm, thus enabling the classification of volcanic and other stratospheric aerosol and cloud layers. Figure  18  shows two examples of V4 CAD performance when classifying stratospheric volcanic layers.
Of these, STS may be thought of as closest to being a liquid aerosol particle. In this section, we assess the CAD classification of PSC layers by comparing V4 results with the classifications from the CALIPSO PSC-specific data product. Figure  20  shows a comparison of extensive PSC layers observed over Antarctica on 15 August 2008. Figure  20a  shows results from the PSC product, with specific colors assigned to the different PSC classifications. Figure  20b  shows the corresponding V4 CAD browse image for the same scene. Generally, there is a good correlation between the spatial distributions of STS in the left panels (red) and layers classified by V4 CAD as aerosols (red). Figure  21  compares the spatial occurrence of stratospheric aerosol identified by the V4 CAD (left panels) and STS from the PSC product (right panels) for the months of June and January 2008 during the Antarctic and Arctic PSC seasons, respectively.
CAD for single-shot layers Unlike the layers detected at 5 km and coarser resolutions (20 and 80 km), which are detected using the 532 nm mea-surements, the single-shot layers at 333 m are detected by the CALIOP algorithm using the 1064 nm measurements between the surface and ∼ 8. 2 km  (Vaughan et al. , 2009). These layers were classified a priori as clouds in all data releases prior to and including V3. This is because, pre-launch, aerosol layers were never expected to have the very high attenuated backscatter coefficients required to be detected at single-shot resolution. However, it has now been established that parts of extended dust layers that are exceptionally dense  can sometimes be legitimately detected at single-shot resolution. Thus, in V4, the CAD algorithm is applied to all layers detected at single-shot resolution.
Conclusions In this paper, we have described the development and implementation of the probability distribution functions (PDFs) and post-processor algorithms used in the CALIOP version 4 (V4) level 2 cloud-aerosol discrimination (CAD) algorithm and provided preliminary performance evaluations via comparisons between the version 3 (V3) and V4 level 2 data products. Like the V3 PDFs, the V4 PDFs are constructed using five different spatial and optical properties: layer-integrated volume depolarization ratio (δ v ), layerintegrated total attenuated color ratio (χ ), layer-mean attenuated backscatter at 532 nm ( β 532 ), latitude, and altitude. In addition, the new PDFs adopt finer spatial resolutions in the latitude and altitude dimensions, eliminate zero-scaling factors at high altitudes, and include high-altitude volcanic aerosols as part of the classification training data.
g. , lofted Asian dust plumes being misidentified as cirrus clouds) have now been largely resolved. This should particularly benefit future studies of the transport of Asian dust to the Arctic in spring, the persistence of smoke at high altitudes, and volcanic aerosols injected into the stratosphere. In the middle to upper troposphere, the V4 data products report a small increase in the fraction of optically thin cirrus clouds (i. e. , cirrus fringes) that are misclassified as aerosol. This outcome is an unfortunate side effect of two highly beneficial improvements: the more accurate calibration of the 532 nm channel leads to more faint layers being detected, and the V4 aerosol PDFs have been redesigned to be more sensitive to the presence of depolarizing aerosol at high altitudes. The extent of these misclassifications is minimized using a newly developed cirrus fringe amelioration algorithm that uses a set of spatial proximity tests to evaluate and, as necessary, override the original aerosol classifications returned by the CAD PDF analyses.
The global distribution of CAD scores for single-shot detections also behaves as expected and desired. The CAD score magnitudes for single-shot aerosol lay-ers are quite low in those places where very dense aerosol layers are not expected (e. g. , over the southern oceans) and substantially higher over desert regions, where dense dust layers are relatively common. Overall, in terms of the development of the PDFs and in the scope of their application, we find that the CALIPSO V4 lidar level 2 data products deliver substantial improvements in global cloud-aerosol discrimination relative to V3, and these more accurate classifications are expected to further improve the science results derived from CALIPSO measurements. Z. Liu et al. :  Discriminating between clouds and aerosols in the CALIOP version 4.
Acknowledgements. This paper is dedicated to the  memory of William H. (Bill) Hunt , pioneering lidar designer and inspirational mentor to the CALIOP algorithm development team. The authors also acknowledge the CALIOP engineers and technicians at  NASA Langley Research Center and Ball Aerospace Technology Corporation. These men and women built a magnificent instrument that continues to perform superbly far, far beyond its original design lifetime. The two referees are thanked for their precious time and encouraging comments. Edited by: James Campbell Reviewed by: two anonymous referees.