Correspondence to. Dr I Abubakar, CDSC Eastern, IPH Xxxxxxxxx XX0 0XX, XX; ibrahim. xxxxxxxx@xxx.xxx.xx Accepted for publication 20 October 2003
Correspondence to. X Xxxxxxx, Department of Neurology, St Elisabeth Hospital, PO Box 90151, 5000 XX Xxxxxxx, The Netherlands; X.Xxxxxxx@Xxxxxxxxx.xx Received 31 March 2003 In revised form 22 June 2003 Accepted 28 June 2003 .......................
Correspondence to. Xx Xxxx Xxxxxxxxx, Department of Family Medicaid insurance dataset Medicine, Oregon Health and Science University, 0000 XX Xxx Xxxxxxx Park Rd, Portland, OR 97329, USA; xxxxxxxx@xxxx.xxx Received 13 September 2013 Revised 23 December 2013 Accepted 20 January 2014 Published Online First 7 February 2014 To cite: Xxxxxxxxx J, Xxxxxx XX, Xxxxxx XX, et al. J Am Med Inform Assoc 2014;21:720–724. BACKGROUND AND SIGNIFICANCE Healthcare organizations are increasingly required to measure and report the quality of care they deliver, for regulatory and reimbursement pur- poses.1–5 Such quality evaluations are often based on insurance claims data,6–8 which have been shown to accurately identify patients with certain diagnoses,9–12 but to be less accurate in identifying services provided, compared to other data sources.6–8 13 14
Correspondence to. Xxxxxxxxx Xxx Xxx Xxxx, Department of Community and Family Medicine, The Chinese University of Hong Kong, 4/F, School of Public Health, Prince of Wales Hospital, Shatin, NT, Hong Kong; xxxxxx@xxxx.xxx.xx Accepted 12 September 2002 .......................
Correspondence to. Dr S Xxxxxxxxxx, Department of Cardiology, Xxxxxxx Hospital, Xxxxxxx Xxxx, Xxxxxxxxx xxxx Xxxx XX0 0XX, XX; xxxxxxxxxxx@xxxxxxx.xxx. uk Submitted 20 February 2005 Accepted 6 April 2005
Correspondence to. Xx Xxxxxxx X Vernon, Eye, Ear, Nose and Throat Centre, Queen’s Xxxxxxx Xxxxxx, Xxxxxxxxxx Xxxxxxxx, Xxxxxxxxxx XX0 0XX, XX; xxxxxxx_xxxxxx@xxxxxxx.xxx Accepted for publication 25 July 2001 ....................... Aims: To assess the intraobserver agreement, interobserver agreement, and the agreement between a digital stereo optic disc camera (Discam) and Heidelberg retina tomograph (HRT) in measuring area cup-disc ratio (ACDR) and radial cup-disc ratio (RCDR) by two observers. Methods: The optic discs of 78 eyes of 39 people (17 cases of primary open angle glaucoma, eight normal tension glaucoma, two ocular hypertension, and 12 normal subjects) were imaged with Discam and HRT. Two observers independently drew the disc margins on the HRT mean topography images and the disc and cup margins on the Discam images. ACDR and the RCDR at various angles were measured with the two systems. Intraobserver agreement, interobserver agreement, and the agreement between the two systems were assessed by 95% tolerance limit of changes (TC) and intraclass correla- tion coefficient (ICC). Results: Eight eyes were excluded due to poor image quality (six Discam and two HRT). 70 eyes were analysed. The intraobserver ACDR agreement was almost perfect in both systems (ICCs = 0.97 and 0.92, and TCs = 11.0% and 15.1% in HRT and Discam respectively). The interobserver ACDR agree- ment was almost perfect in HRT (ICC = 0.97) and substantial in Discam (ICC = 0.79), (TCs = 10.5% and 24.5% respectively). The ACDR agreement between the two systems was substantial in observer A (ICC = 0.67) and moderate in observer B (ICC = 0.53), (TCs = 24.8% and 46.7% respectively). The HRT measured the ACDR significantly larger than the Discam (p <0.001), and the differences were sig- nificantly larger in the glaucomatous group (p <0.001). RCDR agreement between the two systems was fair to substantial in observer A (ICC = 0.36 to 0.74) and slight to moderate in observer B (ICC = 0.12 to 0.45). Both observers achieved the best RCDR agreement between the two systems at the inferior optic disc position.
Correspondence to. Dr X X X X xxx xxx Xxxx, Department of Pathology 437 PA, University Medical Center, St Radboud, PO Box 9101, 6500 HB Nijmegen, The Netherlands; X.xxxxxxxxxx@xxxxxx.xxx.xx Accepted for publication 28 January 2002 ....................... A
Correspondence to. Dr Xxxxxxxx Xxxxxx, Department of Language and Communication Science, City University, Northampton Square, London EC1V 0HB, UK; x.xxxxxx@xxxx.xx.xx Received 18 November 2006 Revised 11 January 2007 Accepted 11 January 2007 Published Online First 26 January 2007 ........................
Correspondence to. Xxxxxxxxxxxxxxx X, Department of Surgery, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand. Phone: +00-00-000000, Fax: +00-00-000000 Email: xxxxxxxx@xxxxx.xxx
Correspondence to. Xxxxxx X. Xxxxx-xxx Xxxxxxxx, Department of Human Genetics 836, Radboud University Medical Center, P.O. Box #9101, Nijmegen 6500 HB, The Netherlands. E-mail: xxxxxx.xxxxx-xxxxxxxxxxx@xxxxxxxxxx.xx Contract grant sponsors: European Commission (GENCODYS grant 241995 under FP7); Dutch Organisation for Health Research and Development ZON-MW (grant 917-86-319); Netherlands Organization for Scientific Research (grant 916-14-043). the same gene in multiple unrelated patients who were a priori not suspected of having the same syndrome, which is usually the case in cohorts of patients with intellectual disability (ID) [Xxx et al., 2014; Xxxxxxxx et al., 2014; Xxxxxxxxx et al., 2016] or autism [X’Xxxx et al., 2012, 2014]. Through this way, many genes for novel genetic disorders have been discovered [Xxxxxxx et al., 2014; Xxxxxxxxxx et al., 2014; Helsmoortel et al., 2014; Vulto-van Xxxxxxxx et al., 2014]. In combination with the ascendance of genomic matchmaking databases that allow the sharing of cases among multiple centers [Sobreira et al., 2015], this technique has greatly boosted gene discovery for highly heterogeneous conditions. The presence of mutations affecting the same gene in multiple unrelated patients is a priori insufficient to establish a causal role for these mutations. Therefore, multiple statistical frameworks have been developed to determine whether a certain number of patients with mutations affecting the same gene is sufficient to establish this gene as a novel disease gene [Xxxxx et al., 2012; X’Xxxx et al., 2012; Xxxxxx et al., 2014; Xxxxxxx et al., 2014; Xxxx et al., 2015; Xxxxxxxxx et al., 2016]. These models, however, are purely based on genetic evidence. Shared phenotype features between patients with mutations in the same gene are typically only considered post-hoc in a nonsystematic fashion. The presence of similar impairments, congenital anomalies, and/or facial dysmorphism among patients with mutations affecting the same gene is accordingly considered supportive for the causality of identified mutations. The additional value of these shared phenotype features can be determined more objectively by incorporating phenotype data in these statistical ap- proaches. This is, however, challenging as this requires phenotype data to be measured in a quantitative manner. Recently, Xxxxx et al. (2015) combined genotype and phenotype enrichments in patients with autosomal-recessive developmental disorders, show- ing increased power to detect potent...
