Adu, K.K. and Adjei, E. (2018). The phenomenon of data loss and cyber security issues in Ghana. Foresight, 20(2), 150–61. Ahmed, M.T.U., Bhuiya, N.I. and Rahman, M.M. (2017). A secure enterprise architecture focused on security and technology-transformation (SEAST), The 12th International Conference for Internet Technology and Secured Transactions, (ICITST-2017), Cambridge, UK, 11–4/12/2017. Alanazi, S.T., Anbar, M., Ebad, S.A., Karuppayah, S. and Al-Ani, H.A. (2020). Theory-based model and prediction analysis of information security compliance behavior in the Saudi healthcare sector. Symmetry, 12(9), 1544. DOI: 10.3390/sym12091544 Alateyah, S.A., Crowder, R.M. and Wills, G.B. (2013). Identified factors affecting the citizen’s intention to adopt e-government in Saudi Arabia. World Academy of Science, Engineering and Technology, 7(8), 904–12. Antonino, P., Duszynski, S., Jung, C. and Rudolph, M. (2010). Indicator-based architecture-level security evaluation in a service-oriented environment. In: The Fourth European Conference on Software Architecture: Copenhagen, Denmark, 23–26/08/2010. DOI: 10.1145/1842752.1842795. Chaturvedi, M., Gupta, M. and Bhattacharya, J. (2008). Cyber Security Infrastructure in India: A Study, Emerging Technologies in E-Government. Available at: http://www.csi-sigegov.org/emerging_pdf/9_70-84.pdf (Accessed on 15/11/2020). Dalol, M.H. (2018). Effectiveness of Accounting Information Systems in Light of Development of IT Infrastructure and Information Security. Master’s Dissertation, The Islamic University of Gaza, Gaza, Palestine. Dooley, K. (2001). Designing Large Scale LANs: Help for Network Designers. USA: O'Reilly Media. Ebad, S. (2018a) An exploratory study of ICT projects failure in emerging markets. Journal of Global Information Technology Management, 21(2), 139–60. DOI: 10.1080/1097198X.2018.1462071. Ebad, S. (2018b). The influencing causes of software unavailability: A case study from industry. Software Practice and Experience, 48(5), 1056–76. DOI: 10.1002/spe.2569. Hashizume, K., Rosado, D.G., Fernández-Medina, E. and Fernandez, E.B. (2013). An analysis of security issues for cloud computing. Journal of Internet Services and Applications, 4(5), n/a. DOI: 10.1186/1869-0238-4-5. Kirby, L. (2015). Beyond Cyber Security: Protecting Your IT Infrastructure. Available at https://uptimeinstitute.com/images/Documents/ProtectingYourITInfrastructure.pdf (accessed on 15/11/2020). Lethbridge, T.C., Sim, S.E. and Singer, J. (2005). Studying software engineers: Data collection techniques for software field studies. Empirical Software Engineering, 10(3), 311–41. Marrone, M. and Kolbe, L.M. (2011). Impact of IT service management frameworks on the IT organization. Business and Information Systems Engineering, 3(1), 5–18. Mastelic, T. and Brandic, I. (2013). TimeCap: Methodology for comparing IT infrastructures based on time and capacity metrics. In: The IEEE 6th International Conference on Cloud Computing, 131–8, Santa Clara, CA, USA, 28/06–03/07/2013. Mimura, M. and Suga, Y. (2019). Filtering malicious JavaScript code with Doc2Vec on an imbalanced dataset. In: The 14th Asia Joint Conference on Information Security (AsiaJCIS), Kobe, Japan, 24–31/08/2019. Pearlson, K.E., Saunders, C.S. and Galletta, D.F. (2019). Managing and Using Information Systems. 5th edition, USA: Wiley. Popp, K. and Meyer, R. (2011). Profit from Software Ecosystems Models, Ecosystems and Partnerships in the Software Industry. Norderstedt, Germany: Books on Demand. Priem, R. (2020). Distributed ledger technology for securities clearing and settlement: Benefits, risks, and regulatory implications. Financial Innovation, 6(11), n/a. DOI: 10.1186/s40854-019-0169-6. Rabii, L. and Abdelaziz, D. (2015). Comparison of e-readiness composite indicators, The 15th International Conference on Intelligent Systems Design and Applications (ISDA), Marrakech, Morocco, 14–16/12/2015. Sanchez-Nielsen, E., Padron-Ferrer, A. and Marreo-Estevez, F. (2011). A multi-agent system for incident management solutions on IT infrastructures. In: The 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2011), La Laguna, Spain, 07–11/11/2011. Schoenfisch, J, Meilicke, C., Stülpnagel, J.V. and Ortmann, J (2018). Root cause analysis in IT infrastructures using ontologies and abduction in Markov logic networks. Information Systems, 74(2), 103–16. Shang, S. and Seddon, P.B. (2000). A comprehensive framework for classifying the benefits of ERP systems. In: The 2000 American Conference of Information Systems, Long Beach, California, 10–13/08/2000. Shoffner, M., Owen, P., Mostafa, J., Lamm, B., Wang, X., Schmitt, C.P. and Ahalt S.C. (2013). The secure medical research workspace: An IT infrastructure to enable secure research on clinical data. Clinical and Translational Science, 6 (3), 222–5. Shrivastava, A.K. (2015). The impact assessment of IT Infrastructure on information security: a survey report. In: International Conference on Inf
Bibliographic record
Abstract
Few epidemiological studies have discussed the gender-specific prevalence of ischemic heart disease (IHD). We aimed to investigate the gender-specific prevalence of IHD among Saudi patients visiting the emergency department and if it is affected by diabetes mellitus and/or hypertension. Three hundred patients were recruited from Prince Sultan Cardiac Center in Al Ahsa, KSA. Hypertension was identified as systolic pressure equal to or more than 140 mmHg and/or diastolic pressure equal to or more than 90 mmHg or by the patient currently being on antihypertensive medication, and coronary artery disease (CAD) was diagnosed by electrocardiogram, cardiac markers, cardiac exercise testing or coronary angiography. Hypertension was found in 80% of males and 72% of females. A significantly higher rate of diabetes was noted in females (62%) compared to males (48%) (p<0.012). Co-existing diabetes and hypertension was found in 70% of females as compared to 38% of males. The occurrence of IHD in males was significantly higher than that in females (p<0.001). However, the incidence of myocardial infarction was greater in females (52%) compared to males (38%) (p<0.035). Co-existing hypertension and diabetes may affect the gender prevalence of myocardial infarction among emergency department patients, with more infarctions being noted among females. This finding helps to guide the treatment strategy for both genders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".