dataesr/scientific-paragraphs-categorization huggingface dataset:
Typus. south africa, KwaZulu-Natal, Hilton, Doreen Clarke Nature Reserve, S29°34' E30°17', on leaves of Pseudarthria hookeri (Leguminosae), 5 Mar. 2013, A. R. Wood (holotype of P. parapseudarthriae CBS H-21710, culture ex-type CPC 23449 = CBS 137996; ITS sequence GenBank KJ869151, LSU sequence GenBank KJ869208, ACT sequence GenBank KJ869229, TEF sequence GenBank KJ869238, MycoBank MB808933). Other specimens examined. south africa, Mpumalanga, Nelspruit, Schagen, on leaves of Pseudarthria hookeri, L. C. C. Liebenberg, 13 July 1936,  PREM 32886. sri laNka (ceyloN), Peradeniya, on Pseudarthria vicida, 20  Dec. 1913, T. Petch No. 4096, holotype of P. pseudarthria K (M).
Typus. chiNa, Beijing, Fragrant Hill, N39°59'18. 4" E116°11'25", on  needles of Pinus tabulaeformis (Pinaceae), 1 Sept. 2013, P. W. Crous & Y. Zhang (holotype CBS H-21718, culture ex-type CPC 23629 = CBS 138004; ITS sequence GenBank KJ869159, LSU sequence GenBank KJ869216, Myco-Bank MB808943).
Typus. chiNa, Beijing, Badaling, N40°20'45. 1" E116°00'48. 3", on  leaf dieback of Hemerocallis fulva (Hemerocallidaceae), 1 Sept. 2013, P. W. Crous & Y. Zhang (holotype CBS H-21721, culture ex-type CPC 23643 = CBS 138007; ITS sequence GenBank KJ869162, LSU sequence GenBank KJ869219, MycoBank MB808948).
Typus. italy, Lecce, on leaves of Nerium oleander (Apocynaceae), 16 Oct. 2013, I. Camele (holotype CBS H-21724, culture ex-type CPC 23765 = CBS 138010; ITS sequence GenBank KJ869165, LSU sequence GenBank KJ869222, ACT sequence GenBank KJ869231, TEF sequence GenBank KJ869240, MycoBank MB808953).
Typus. south africa, Eastern Cape Province, Grahamstown, on Aloe sp. (Aloaceae), 26 July 2011, P. W. Crous (holotype CBS H-21727, culture ex-type CBS 137940 = CPC 19705; ITS sequence GenBank KJ817888, LSU sequence GenBank KJ817890, BTUB = KJ817886, MycoBank MB808881); CPC 19706 = CBS 137941, ITS = KJ817889, LSU = KJ817891, BTUB = KJ817887.
Typus. spaiN, Cantabria, Ucieda, Saja-Besaya Natural Park, on unidentified dead wood, 27 Sept. 2010, coll. M. HernándezRestrepo, J. Mena & J. Guarro, isol. M. Hernández-Restrepo (holotype CBS H-21464, cultures ex-type FMR 11245 = CBS 137253; ITS sequence GenBank KF924559, SSU sequence GenBank HF937347, LSU sequence GenBank KF924559, MycoBank MB807097). Fungal Planet 271 -10 June 2014 Conioscypha minutispora Hern. -Restr. , Gené & Guarro, sp. nov. Colour illustrations. Forest in the Saja-Besaya Natural Park where the sample was collected. Photo plate: a. Conidiogenous cells with conidia; b. conidia. Scale bars a, b = 5 µm. Line drawing: a'. Habit; b'. conidiogenous cell with dark conidia; c'. conidia. Scale bar b'-c' = 5 µm.
Typus. uruGuay, Quebrada de los Cuervos, on Myrcianthes pungens, Oct. 2012, M. J. Wingfield & C. Perez (holotype PREM 60964, culture exholotype CBS 134695 = CMW 39055, ex-isotypes CMW 39018 -39020; ITS sequence of CBS 134695 GenBank KJ834524, LSU sequence of CBS 134695 GenBank KJ834525, MycoBank MB808837).
Typus. fraNce, Côte d'Or, Corcelles-Les-Monts, Bois de La Combe au Pré, 25 Nov. 2008, on dead stem and cladodes of Ruscus aculeatus, A. Gar diennet (holotype deposited at Faculté de Pharmacie de Lille, France (LIP) AG08NE22, culture ex-type CBS126108; ITS GenBank sequence KJ802123 & KJ802124, MycoBank MB808720).
Typus. spaiN, Gran Canaria, Pico de Osorio, from forest soil, 10 Aug. 2009, coll. M. Calduch & A. M. Stchigel, isol. Y. Marín (holotype CBS H-21353, cultures ex-type CBS 135818 = FMR 12156; other strain CBS 136362 = FMR 12323; LSU sequences of CBS 135818 and CBS 136362 GenBank HG326871 and HG326872, respectively; Tub sequences of CBS 135818 and CBS 136362 GenBank HG423208 and HG423209, respectively; and tef-1 sequences of CBS 135818 and CBS 136362 GenBank HG326875 and HG326876, respectively; MycoBank MB804881).
Typus. australia, Queensland, Brisbane Forest Park, S27°17'36. 2" E152°44'42. 0", on Cycas sp. (Cycadaceae) leaves, 15 July 2009, P. W. Crous & R. G. Shivas (holotype CBS H-21679, culture ex-type CPC 17215 = CBS 137968; ITS sequence GenBank KJ869119, LSU sequence GenBank KJ869177, TEF sequence GenBank KJ869235, MycoBank MB808894); CPC 17217, ITS sequence GenBank KJ869120.
Typus. australia, Queensland, Cairns Botanical Garden, S16°53'57. 1" E145°44'48. 7", on Cycas media (Cycadaceae) leaves, 12 Aug. 2009, P. W. Crous & R. G. Shivas (holotype CBS H-21681, culture ex-type CPC 17251 = CBS 137970; ITS sequence GenBank KJ869122, LSU sequence GenBank KJ869179, ACT sequence GenBank KJ869227, TEF sequence GenBank KJ869236, MycoBank MB808896).
Typus. australia, Queensland, Brisbane, Slaughter Falls, on Macrozamia (Zamiaceae) leaf litter, 16 July 2009, P. W. Crous & R. G. Shivas (holotype CBS H-21682, culture ex-type CPC 17262 = CBS 137971; ITS sequence GenBank KJ869123, LSU sequence GenBank KJ869180, MycoBank MB808897).
Typus. australia, New South Wales, Nightcap National Park, S28°38'41. 3" E153°20'17. 9", on Neolitsea australiensis (Lauraceae) leaves, 9 Mar. 2013, B. A. Summerell (holotype CBS H-21686, culture ex-type CPC 22168 = CBS 137974; ITS sequence GenBank KJ869126, LSU sequence GenBank KJ869183, MycoBank MB808901).
Typus. ethiopia, Addis Ababa, Mangadishu Forest, N8°58'01. 8" E38°32' 56. 4", on needles of Juniperus procera (Cupressaceae), 25 June 2013, P. W. Crous & A. Assefa (holotype CBS H-21706, culture ex-type CPC 23276 = CBS 137992; ITS sequence GenBank KJ869147, LSU sequence GenBank KJ869204, MycoBank MB808929). Juniperus stand in Mangadishu Forest, Ethiopia; conidioma, conidiophores and conidia in vivo. Scale bars = 10 µm.
Typus. fraNce, Nice, L'aire d'Esterel petrol filling station, on needles of Pinus sp. (Pinaceae), 20 July 2013, P. W. Crous (holotype CBS H-21711, culture ex-type CPC 23455 = CBS 137997; ITS sequence GenBank KJ869152, LSU sequence GenBank KJ869209, ACT sequence GenBank KJ869230, TUB sequence GenBank KJ869249, MycoBank MB808934).
Typus. chiNa, Beijing, Fragrant Hill, N39°59'18. 4"E116°11'25", onneedles of Pinus tabulaeformis (Pinaceae), 1 Sept. 2013, P. W. Crous & Y. Zhang (holotype CBS H-21717, culture ex-type CPC 23656 = CBS 138003; ITS sequence GenBank KJ869158, LSU sequence GenBank KJ869215, Myco-Bank MB808942).
Typus. chiNa, Beijing, Badaling, N40°20'45. 1" E116°00'48. 3", on needles of Pinus sp. (Pinaceae), 1 Sept. 2013, P. W. Crous & Y. Zhang (holotype CBS H-21719, culture ex-type CPC 23639 = CBS 138005; ITS sequence GenBank KJ869160, LSU sequence GenBank KJ869217, MycoBank MB808944).
, as Inocybe sp. P156). Inocybe lanatopurpureaEsteve-Rav. & G. Moreno, sp. nov. Additional specimens examined. spaiN, Castilla-La Mancha, Guadalajara province, Aldeanueva de Atienza, river Pelagallinas, N41°11'34" W3°5'58", 1380 m, in humus of Pinus sylvestris and P. pinaster forest, in acid soil, 12 Oct. 2006, F. EsteveRaventós, G. Moreno, F. Prieto, F. D. Calonge, M. A. González & M. Heykoop, AH 34456, ITS sequence GenBank KJ432289; Andalucía, Jaén province, Santa Elena, road to Miranda del Rey Km 4, Llano de las Américas, path to Barranco del Lobo, N38°21'8" W3°33'44", 760 m, in humus of re-forested woodland of Pinus pinaster and P. radiata, 28 Nov. 2009, F. EsteveRaventós & J. D. Reyes, AH 36498, ITS sequence GenBank KJ432291.
Financial support for  R. Cheewangkoon  was provided by the  Thailand Research Fund   MRG5580163. Christian Lechat ,  Alain Gardiennet  and  Jacques Fournier  gratefully acknowledge  Dr Amy Rossman  for her invaluable scientific help and relevant suggestions in the description of  Pseudonectria rusci. Allison K. Walker  was supported by a  National Sciences and Engineering Research Council of Canada Collaborative Research and Development Grant  to  J. David Miller  and Keith A.
Typus. ethiopia, Addis Ababa, Mangadishu Forest, N8°58'01. 8" E38°32' 56. 4", on needles of Juniperus procera (Cupressaceae), 25 June 2013, P. W. Crous & A. Assefa (holotype CBS H-21706, culture ex-type CPC 23276 = CBS 137992; ITS sequence GenBank KJ869147, LSU sequence GenBank KJ869204, MycoBank MB808929). Juniperus stand in Mangadishu Forest, Ethiopia; conidioma, conidiophores and conidia in vivo. Scale bars = 10 µm. Pedro W. Crous & Johannes Z. Groenewald, CBS-KNAW Fungal Biodiversity Centre, P. O. Box 85167, 3508 AD Utrecht, The Netherlands; e-mail: p. crous@cbs. knaw. nl & e. groenewald@cbs. knaw. nl Addisu Assefa, Department of Biology, Madawalabu University, P. O. Box 247, Bale Robe, Ethiopia; e-mail: addddisu@yahoo. com Colour illustrations.
Colour illustrations. Inner garden next to the Dom tower in Utrecht, The Netherlands; synnematous conidiophores, conidiogenous cells, conidiophores with rhizoids and conidia in culture. Scale bars = 10 µm. Pedro W. Crous & Johannes Z. Groenewald, CBS-KNAW Fungal Biodiversity Centre, P. O. Box 85167, 3508 AD Utrecht, The Netherlands; e-mail: p. crous@cbs. knaw. nl&e. groenewald@cbs. knaw. nlFungalPlanet 268 -10 June 2014CephalotrichiellaCrous, gen. nov. Typus. NetherlaNds, Utrecht, isolated from air, Dec. 2013, P. W. Crous (holotype CBS H-21725, culture ex-type CPC 23865 = CBS 138011; ITS sequence GenBank KJ869166, LSU sequence GenBank KJ869223, Myco- Bank MB808955).
nephrospora (8-11 × 4. 5-6 µm; Hansford 1943). Based on megablast search of NCBIs GenBank nucleotide database, the closest hits using the ITS (GenBank KJ817888) sequence are S. nephrospora (GenBank AF081476; Identities = 633/642 (99 %), Gaps = 4/642 (0 %)) and S. microspora (Gen- Bank AF081475; Identities = 591/646 (91 %), Gaps = 10/646 (1 %)). Closest hits using LSU (GenBank KJ817890) sequences are Didymostilbe matsushimae (GenBank AY283545; Identi- ties = 810/836 (97 %), Gaps = 6/836 (0 %)), Melanopsamma pomiformis (GenBank AY489709; Identities = 810/836 (97 %), Gaps = 7/836 (0 %)) and Stachybotrys echinata (GenBank AY489736; Identities = 806/834 (97 %), Gaps = 5/834 (0 %)). Closest hit using β-tubulin (GenBank KJ817886) sequences are Cylindrocladiella infestans (GenBank JN098762; Identities = 319/367 (87 %), Gaps = 14/367 (3 %)) and Cylindrocladiella var iabilis (GenBank JN098722; Identities = 319/367 (87 %), Gaps = 16/367 (3 %)).
, as Inocybe sp. P156). Inocybe lanatopurpureaEsteve-Rav. & G. Moreno, sp. nov. Additional specimens examined. spaiN, Castilla-La Mancha, Guadalajara province, Aldeanueva de Atienza, river Pelagallinas, N41°11'34" W3°5'58", 1380 m, in humus of Pinus sylvestris and P. pinaster forest, in acid soil, 12 Oct. 2006, F. EsteveRaventós, G. Moreno, F. Prieto, F. D. Calonge, M. A. González & M. Heykoop, AH 34456, ITS sequence GenBank KJ432289; Andalucía, Jaén province, Santa Elena, road to Miranda del Rey Km 4, Llano de las Américas, path to Barranco del Lobo, N38°21'8" W3°33'44", 760 m, in humus of re-forested woodland of Pinus pinaster and P. radiata, 28 Nov. 2009, F. EsteveRaventós & J. D. Reyes, AH 36498, ITS sequence GenBank KJ432291. Fungal Planet 273 -10 June 2014 Typus. spaiN, Andalucía, Jaén province, Santa Elena, road to Miranda del Rey km 4, Llano de las Américas, path to Barranco del Lobo, N38°21'8" W3°33'44", 760 m, in humus of re-forested woodland of Pinus pinaster and P.
Acknowledgements. We thank  Luis Mancini  and  Ana Carolina Miranda  for their support during the stable isotope data acquisition at the  Universidade de Brasília  and  Osmar Antunes  for his support at  Universidade de São Paulo. We also thank  Augusto Auler ,  Daniel Menin  and the  Espeleo Club Andino (ECA Perú)  for their support during the field work. This work was undertaken as part of the the  PALEOTRACES  project ( IRD-UFF-UANTOF ),  HYBAM  Project (IRD) and  PRIMO cooperative project  ( CNPq-IRD ), and supported by the  Fundação de Amparo a Pesquisa do Estado de Rio de Janeiro, Brazil  ( FAPERJ  grant  E-26/100. 377/2012 ), Fundação de Amparo a Pesquisa do Estado de São Paulo, Brazil  ( FAPESP  grants  2011/39450394  to  F. W. Cruz  and  2012/50260-6  and  University of São Paulo  through  INCLINE research group ) and also by grants  2013CB955902 ,  CNSF 41230524 ,  US NSF  grants  0502535  and  3961103404  to  L.
Background: Recently, collaboration between regulators and payers was set up and was mainly focused on evidence generation along product clinical development. However, neither the regulatory path nor the new active substance status (NASs) was considered. Granting NASs will provide the product with 8 years of data protection and 2 years of market exclusivity during which no generic could enter the market. Objective: To review the economic impact (for payers) of NASs granted by the European Medicines Agency (EMA) for dimethyl fumarate (DMF), developed by Biogen and approved for multiple sclerosis (MS) as Tecfidera † on 3 February 2014. Method: We reviewed the available DMF-containing products and identified their indication and price through relevant databases and official Web sites. The economic impact of Tecfidera † on payers' budgets was calculated assuming NASs was or was not granted.
Method We searched for the availability and indication of DMF products in the EU through national medical agencies, health insurance, and HTA Web sites. This was complemented by literature searches to identify approved indications and common usage of DMF in EU countries. We searched for relevant information to identify, where possible, the price of Tecfidera † in the countries where it was launched. In countries where it was available, the pharmacy retail prices were identified through publicly available pricing databases and official gazette databases. In addition, we searched in company Web sites, press releases, and EU HTA Web sites in order to collect appropriate information related to the potential price of Tecfidera † in the EU. We reviewed the forecasts as provided by syndicated reports on MS (Datamonitor Healthcare, https://service.
Methods -We searched PubMed/Medline, Cochrane Central Register of Controlled Trials, and ClinicalTrials. gov for RCTs comparing medications with either placebo or other drugs in the treatment of A/A in AD, between January 2008 and December 2013. Results-We identified a total of 18 RCTs; of these, 11 were completed and 7 ongoing. Of the ongoing RCTs, only one is in Phase III. Seven of 10 completed RCTs with reported results did not.
METHODS Reports of RCTs of medications for the treatment of A/A in AD published in the English language were identified by searching PubMed between January 2008 and December 2013, using terms ("Dementia"[Mesh] OR "Alzheimer"[Mesh]) AND ("Clinical Trial"[Mesh] OR "therapeutics"[MeSH Terms]) AND ("Agitation"[Mesh] OR "Aggression"[Mesh]) OR "Behavioral symptoms"[Mesh]). Free text was used to identify articles on "neuropsychiatric symptoms", "treatment for neuropsychiatric behaviors" and "behavioral and psychological symptoms of dementia. " This search was supplemented by hand searching of reference lists of selected articles, meta-analyses, and review articles. Google Scholar was searched for additional articles, especially of ongoing RCTs and new drugs. In addition, we searched the Cochrane Central Register of Controlled Trials and ClinicalTrials.
For the purpose of the validation of the Stroke Riskometer TM we used a sub-set of ARCOS IV data on strokes in people aged 21-95 years (n = 410). The Rotterdam Study has been described previously  (3). It is an ongoing prospective population-based cohort study that focuses on the causes and consequences of chronic and disabling diseases in the elderly  (31). The cohort started enrolment in 1990 and included 7983 participants aged ≥55 years living in Ommoord, a district of the city of Rotterdam in the Netherlands (participation rate 78%). Follow-up was complete until January 1, 2012, for 97•1% of potential person-years  (32). The Rotterdam study contributed data from n = 7713 individuals who ranged in age from 55-90 years. Russian cohort studies were conducted in Moscow (n = 412), Ulyanovsk (n = 512), Nal'chik (n = 177) and Minsk (n = 277) over various time periods starting from 1992.
Stroke risk factors and algorithm development Risk scores from three stroke predictors were generated. Each scoring algorithm utilized a series of known or hypothesized stroke risk factors (Table  1 ), some of which are in addition to those used in the FSRS and are the central targets in the new WHO Global Action Plan for the NCD 2013-2020  (34). Distribution of each risk factor for each data set is listed in Table  2. The Stroke Riskometer TM algorithm was derived from the Framingham Stroke Risk Score (FSRS) prediction algorithm  (7)  but enhanced to include several additional major risk factors shown to be important for both ischaemic and haemorrhagic strokes, largely based on the INTERSTROKE study  (4). The additional variables are listed in Table  1. Questions were based on recall such as 'Have you ever been told by a doctor that you have atrial fibrillation (irregular heartbeats)?' and 'Have you ever been told by a doctor that you have left ventricular hypertension?' such that no immediate medical test (e.
Algorithm validation The performance of the Stroke Riskometer TM was tested across three data sets (ARCOS, Russian and Rotterdam) as greater precision is gained when assessing risk prediction models using multiple epidemiologic studies compared to single-studies  (49). We also compared performance of the Stroke Riskometer TM with the FSRS  (7)  and QStroke (  8 ) risk score equations. The five-year estimated risk of stroke for Russian and Rotterdam cohorts was calculated across the three different prediction algorithms. Estimates for 10-year stroke risk score were generated only for the Rotterdam study where data were available over a span of 10 years. Follow-up data for the ARCOS were limited to one-year and for Russian data sets -4 to 12 years. We calculated Harrels C-statistic and Somer's D-statistic to measure discrimination (the ability of the algorithms to discriminate between stroke and nonstroke events).
Validation cohorts A total of 752 new strokes that developed in a sample of 9501 individuals over the follow-up period (80 308 person-years of observation) across three studies (ARCOS, Russia and Rotterdam) were utilized to investigate the recently derived stroke risk prediction tool algorithm Stroke Riskometer TM against two established stroke risk score prediction algorithms [FSRS  (7)  and QStroke  (8) ]. The three data sets differed in their distribution of stroke outcomes and predictor variables required for each algorithm. The ARCOS data set was comprised of stroke only data whilst the Russian database was generated through a new cohort study, with 3•2% total strokes being observed. Of the Rotterdam study, 8•4% was comprised of strokes. None of the three studies had all variables required for the Stroke Riskometer TM algorithm.
The Russian data set was the most recent of the three data sets analyzed here so the average age was lowest (50 years for males and females; Table  2 ). Individuals in ARCOS and the Rotterdam study were similar in age (males 69 years and females 72 years). The Russian data set had the lowest average systolic blood pressure (SBP) while ARCOS had the highest. Both the Rotterdam and ARCOS studies had similar SBP values for males and females whilst the Russian data set had higher values in males. BMI was not recorded in ARCOS but was similar in males and females of the Russian data set (average for males = 27•8 kg/m 2 and females = 27•5 kg/m 2 ) and comparable with males and females in the Rotterdam study (average for males = 25•6 kg/m 2 and females = 26•7 kg/m 2 ). As BMI was not recorded in ARCOS waist circumference was used (for the Stroke Riskometer TM algorithm); women had greater waist circumference (99 cm) than men (97 cm).
Due to inclusion of only patients with stroke, the ARCOS database had the highest percentage of individuals with diabetes (20%) compared to the Russian data set (range 4•4-4•8%) and Rotterdam (range 6•1-7•1%). A much higher proportion of the ARCOS database had individuals with a history of CVD, previous stroke/TIA event and were of non-European descent, compared to the Russian and Rotterdam cohorts (Table  2 ).
Validation and overall performance of the Stroke Riskometer TM As none of the three studies had all variables required for the Stroke Riskometer TM algorithm, we cannot fully validate this algorithm with the emphasis for the continuing development of the Stroke Riskometer TM algorithm. We present measures of overall performance, discrimination and calibration of the Stroke Riskometer TM algorithm based on available data. The FSRS and Stroke Riskometer TM algorithms gave comparable 5-year and 10-year risk scores for males and females within each data set (Fig. 1 ). Risk scores differed substantially by data set, reflecting the availability of predictors within each cohort. Each algorithm (FSRS, Stroke Riskometer TM and QStroke) explained 50% of the variation observed in the ARCOS data set (R 2 statistic, Table  3 ).
In the Russian database we observed that both the Stroke Riskometer TM and FSRS algorithms classified most participants as high risk (63•6% five-year risk in males and 78•4% five-year risk in females). As ARCOS had all stroke events we would expect these to predominately to be categorized as 'High risk' this is observed for FSRS and Stroke Riskometer TM (males = 98•1% and females = 86•4%). A very high proportion of individuals in the ARCOS data set were classified as high risk for Stroke Riskometer TM but low risk on QStroke (males = 97•2% and females = 48•1% for five-year risk). A high proportion of females in the Rotterdam study were categorized as low-risk for Stroke Riskometer TM and high-risk for QStroke (13•2% for five-year risk in females) compared to 5•5% of males classified as low-risk for Stroke Riskometer TM and high-risk for QStroke (Table  5 ).
Calibration Calibration plots of the predicted risk scores against the observed event for each tenth of predicted risk, separately for males and females, are shown in Fig. 3  (all data sources) and Supplementary Fig. S2  (Russian and Rotterdam cohorts). The Russian cohort illustrated that the QStroke algorithm was better calibrated for 0•99 (0•08-3•14) 0•32 (0•001-1•54) 0•72 (0•21-1•81) 1•85 (0•89-3•40) C statistic Stroke event data only 0•515 (0•514-0•516) 0•514 (0•513-0•514) 0•511 (0•511-0•511) 0•513 (0•512-0•513) D statistic 0•030 (0•029-0•031) 0•029 (0•028-0•029) 0•022 (0•022-0•023) 0•027 (0•026-0•027) AUROC 68•1 (58•5-77•7) 77•4 (69•2-85•6) 63•6 (58•5-68•5) 65•4 (61•0-69•7) 10 R 2 (%) 0•91 (0•34-1•85) 0•91 (0•34-1•85) C statistic 0•517 (0•517-0•517) 0•522 (0•521-0•522) D statistic 0•033 (0•032-0•033) 0•045 (0•044-0•045) AUROC 60•4 (58•8-64•0) 64•6 (61•6-67•6) QStroke 5 R 2 (%) 49•79 (49•73-50•3) 49•98 (49•88-50•04) 5•22 (1•54-14•22) 2•49 (0•24-9•77) 1•04 (0•43-2•13) 1•26 (0•60-2•36) C statistic Stroke event data only 0•526 (0•524-0•527) 0•511 (0•511-0•512) 0•513 (0•513-0•513) 0•515 (0•515-0•515) D statistic 0•051 (0•050-0•052) 0•023 (0•022-0•023) 0•027 (0•027-0•027) 0•031 (0•030-0•031) AUROC 80•6 (72•3-88•9) 71•2 (59•2-83•8) 66•1 (61•9-70•2) 69•7 (66•3-73•1) 10 R 2 (%) 0•97 (0•41-1•92) 0•97 (0•41-1•92) C statistic 0•520 (0•519-0•520) 0•526 (0•526-0•526) D statistic 0•039 (0•039-0•039) 0•053 (0•053-0•053) AUROC 62•5 (59•4-65•7) 67•6 (65•0-70•1) the data set of all three algorithms, for a database with few strokes (Supplementary Fig.
S2 ). An improved calibration for the FSRS and Stroke Riskometer TM algorithms compared to QStroke was observed for the Rotterdam data set, particularly for females. The QStroke algorithm was shown to over-estimate stroke risk in females whilst FSRS and Stroke Riskometer TM over-estimated stroke risk in males (Supplementary Fig. S2 ). Visual assessment of five-year risk scores from the combined data (ARCOS, Russia and Rotterdam) highlighted that the Stroke Riskometer TM algorithm was better calibrated compared to QStroke, especially for females (Fig. 3 ). All predicted risk scores were not well calibrated to our data sets (Table  6 , H-L tests P < 0•006).