Achieving High Research Reporting Quality Through the Use of Computational Ontologies
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Systematic reviews and meta-analyses constitute one of the central pillars of evidence-based medicine. However, clinical trials are poorly reported which delays meta-analyses and consequently the translation of clinical research findings to clinical practice. We propose a Center of Excellence in Research Reporting in Neurosurgery (CERR-N) and the creation of a clinically significant computational ontology to encode Randomized Controlled Trials (RCT) studies in neurosurgery. A 128 element strong computational ontology was derived from the Trial Bank ontology by omitting classes which were not required to perform meta-analysis. Three researchers from our team tagged five randomly selected RCT’s each, published in the last 5 years (2004–2008), in the Journal of Neurosurgery (JoN), Neurosurgery Journal (NJ) and Journal of Neurotrauma (JoNT). We evaluated inter and intra observer reliability for the ontology using percent agreement and kappa coefficient. The inter-observer agreement was 76.4%, 75.97% and 74.9% and intra-observer agreement was 89.8%, 80.8% and 86.56% for JoN, NJ and JoNT respectively. The inter-observer kappa coefficient was 0.60, 0.54 and 0.53 and the intra-observer kappa coefficient was 0.79, 0.82 and 0.79 for JoN, NJ and JoNT journals respectively. The high degree of inter and intra-observer agreement confirms tagging consistency in sections of a given scientific manuscript. Standardizing reporting for neurosurgery articles can be reliably achieved through the integration of a computational ontology within the context of a CERR-N. This approach holds potential for the overall improvement in the quality of reporting of RCTs in neurosurgery, ultimately streamlining the translation of clinical research findings to improvement in patient care.
KeywordsSystematic review Meta-analyses Evidence-based medicine Reporting RCT Neurosurgery Standardized Ontology Kappa coefficient
We would like to thank the Research on Research group (www.researchonresearch.org), Duke University, USA for manuscript writing templates. (Phadtare et al. 2009). We would also like to thank FS from the Cochrane group who helped us modify the ontology, by giving us input as to which classes would be required for a meta-analysis.
- Buttram, S., Wisniewski, S., Jackson, E., Adelson, D., Feldman, K., Bayir, H., et al. (2007). Multiplex assessment of cytokine and chemokine levels in cerebrospinal fluid following severe pediatric traumatic brain injury: effects of moderate hypothermia. Journal of Neurotrauma, 24(11), 1707–1718. doi: 10.1089/neu.2007.0349.PubMedCrossRefGoogle Scholar
- Fonseca, F., & Martin, J. (2007). Learning the differences between ontologies and conceptual schemas through ontology-driven information systems. JAIS - Journal of Association for Information Systems - Special Issue on Ontologies in the Context of IS, 8(2), 129–142.Google Scholar
- Gruber, T. (2008). Ontology. Entry in the Encyclopedia of Database Systems. Ling Liu and M. Tamer Özsu (Eds.), Springer-Verlag, to appear in 2008. Provides a definition of ontology as a technical term for computer science, tracing its historical context from philosophy and AI.Google Scholar
- Gruber, T. R. (1993). Toward principles for the design of ontologies used for knowledge sharing.Google Scholar
- Jiang, J. Y., Wei, X. U., Li, W. P., Xu, W. H., Jun, Z., Bao, Y. H., et al. (2005). Efficacy of standard trauma craniectomy for refractory intracranial hypertension with severe traumatic brain injury: a multicenter, prospective, randomized controlled study. Journal of Neurotrauma, 22(6), 623–628.PubMedCrossRefGoogle Scholar
- Manser, R., & Walters, E. H. (2001). What is evidence-based medicine and the role of the systematic review: the revolution coming your way. Monaldi Archives for Chest Diseases, 56(1), 33–38.Google Scholar
- Marmarou, A., Guy, M., Murphey, L., Roy, F., Layani, L., Combal, J. P., et al. (2005). A single dose, three-arm, placebo-controlled, phase I study of the bradykinin B2 receptor antagonist Anatibant (LF16-0687Ms) in patients with severe traumatic brain injury. Journal of Neurotrauma, 22(12), 1444–1455.PubMedCrossRefGoogle Scholar
- Noy, N. F., & McGuinness, D. L. (2001). Ontology Development 101: A guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report. SMI-2001-0880.Google Scholar
- Racer Pro 2.0 available at: http://www.racer-systems.com/index.phtml.
- Shankar, R. D., Martins, S. B., O’Connor, M., Parrish, D. B., & Das, A. K. (2007). An ontology-based architecture for integration of clinical trials management applications. AMIA Annu Symp Proc. 661–665.Google Scholar
- Tendal, B., Higgins, J. P., Jüni, P., Hrobjartsson, A., Trelle, S., Nüesch, E. et al. (2008). Observer variation when extracting data for the calculation of a standardized mean difference, Poster presentation at 16th Cochrane Colloquium, Freiburg, 3-7 October 2008.Google Scholar
- Tseng, M. Y., Hutchinson, P. J., Turner, C. L., Czosnyka, M., Richards, H., Pickard, J. D., et al. (2007). Biological effects of acute pravastatin treatment in patients after aneurysmal subarachnoid hemorrhage: a double-blind, placebo-controlled trial. Journal of Neurosurgery, 107, 1092–1100.PubMedCrossRefGoogle Scholar
- Vajkoczy, P., Meyer, B., Weidauer, S., Raabe, A., Thome, C., Ringel, F., et al. (2005). Clazosentan (AXV-034343), a selective endothelin A receptor antagonist, in the prevention of cerebral vasospasm following severe aneurysmal subarachnoid hemorrhage: results of a randomized, double-blind, placebo-controlled, multicenter Phase IIa study. Journal of Neurosurgery, 103, 9–17.PubMedCrossRefGoogle Scholar
- Willems, P. W., Taphoorn, M. J., Burger, H., Berkelbach van der Sprenkel, J. W., & Tulleken, C. A. (2006). Effectiveness of neuronavigation in resecting solitary intracerebral contrast-enhancing tumors: a randomized controlled trial. Journal of Neurosurgery, 104, 360–368.PubMedCrossRefGoogle Scholar