Achieving High Research Reporting Quality Through the Use of Computational Ontologies
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 RCTs each, published in the last 5years (20042008), 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.
reporting ontology systematic review meta-analyses evidence-based medicine neurosurgery rct kappa coefficient standardized