The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Developing an instrument to assess the endoscopic severity of ulcerative colitis: the Ulcerative Colitis Endoscopic Index of Severity (UCEIS)
Open accessAbstract
Background: Variability in endoscopic assessment necessitates rigorous investigation of descriptors for scoring severity of ulcerative colitis (UC). Objective: To evaluate variation in the overall endoscopic assessment of severity, the intra- and interindividual variation of descriptive terms and to create an Ulcerative Colitis Endoscopic Index of Severity which could be validated. Design: A two-phase study used a library of 670 video sigmoidoscopies from patients with Mayo Clinic scores 0–11, supplemented by 10 videos from five people without UC and five hospitalised patients with acute severe UC. In phase 1, each of 10 investigators viewed 16/24 videos to assess agreement on the Baron score with a central reader and agreed definitions of 10 endoscopic descriptors. In phase 2, each of 30 different investigators rated 25/60 different videos for the descriptors and assessed overall severity on a 0–100 visual analogue scale. κ Statistics tested inter- and intraobserver variability for each descriptor. A general linear mixed regression model based on logit link and β distribution of variance was used to predict overall endoscopic severity from descriptors. Results: There was 76% agreement for ‘severe’, but 27% agreement for ‘normal’ appearances between phase I investigators and the central reader. In phase 2, weighted κ values ranged from 0.34 to 0.65 and 0.30 to 0.45 within and between observers for the 10 descriptors. The final model incorporated vascular pattern, (normal/patchy/complete obliteration) bleeding (none/mucosal/luminal mild/luminal moderate or severe), erosions and ulcers (none/erosions/superficial/deep), each with precise definitions, which explained 90% of the variance (pR 2, Akaike Information Criterion) in the overall assessment of endoscopic severity, predictions varying from 4 to 93 on a 100-point scale (from normal to worst endoscopic severity). Conclusion: The Ulcerative Colitis Endoscopic Index of Severity accurately predicts overall assessment of endoscopic severity of UC. Validity and responsiveness need further testing before it can be applied as an outcome measure in clinical trials or clinical practice.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Cited by
Lamb, C. A., Kennedy, N. A., Raine, T., Hendy, P. A., Smith, P. J., Limdi, J. K., Hayee, B., Lomer, M. C. E., Parkes, G. C., Selinger, C., et al. (2019) · Gut · doi:10.1136/gutjnl-2019-318484 · Cited by 2,346
Magro, F., Gionchetti, P., Eliakim, R., Ardizzone, S., Armuzzi, A., Barreiro-de Acosta, M., Burisch, J., Gecse, K. B., Hart, A. L., Hindryckx, P., et al. (2017) · Journal of Crohn's and Colitis · doi:10.1093/ecco-jcc/jjx008 · Cited by 1,910
Paramsothy, S., Kamm, M. A., Kaakoush, N. O., Walsh, A. J., van den Bogaerde, J., Samuel, D., Leong, R. W. L., Connor, S., Ng, W., Paramsothy, R., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30182-4 · Cited by 1,276
Stidham, R. W., Liu, W., Bishu, S., Rice, M. D., Higgins, P. D. R., Zhu, J., Nallamothu, B. K., & Waljee, A. K. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.3963 · Cited by 285
Haifer, C., Paramsothy, S., Kaakoush, N. O., Saikal, A., Ghaly, S., Yang, T., Luu, L. D. W., Borody, T. J., & Leong, R. W. (2022) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(21)00400-3 · Cited by 285
Takenaka, K., Ohtsuka, K., Fujii, T., Negi, M., Suzuki, K., Shimizu, H., Oshima, S., Akiyama, S., Motobayashi, M., Nagahori, M., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.02.012 · Cited by 266
Maeda, Y., Kudo, S.-E., Mori, Y., Misawa, M., Ogata, N., Sasanuma, S., Wakamura, K., Oda, M., Mori, K., & Ohtsuka, K. (2019) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2018.09.024 · Cited by 245
Current best practice for disease activity assessment in IBD
Walsh, A. J., Bryant, R. V., & Travis, S. P. L. (2016) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2016.128 · Cited by 223
Self-citationConverging goals of treatment of inflammatory bowel disease from clinical trials and practice
Levesque, B. G., Sandborn, W. J., Ruel, J., Feagan, B. G., Sands, B. E., & Colombel, J.-F. (2015) · Gastroenterology · doi:10.1053/j.gastro.2014.08.003 · Cited by 217
Self-citationCentral reading of ulcerative colitis clinical trial videos using neural networks
Gottlieb, K., Requa, J., Karnes, W., Chandra Gudivada, R., Shen, J., Rael, E., Arora, V., Dao, T., Ninh, A., & McGill, J. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.10.024 · Cited by 136
Iacucci, M., Parigi, T. L., Del Amor, R., Meseguer, P., Mandelli, G., Bozzola, A., Bazarova, A., Bhandari, P., Bisschops, R., Danese, S., et al. (2023) · Gastroenterology · doi:10.1053/j.gastro.2023.02.031 · Cited by 127
Fully automated endoscopic disease activity assessment in ulcerative colitis
Yao, H., Najarian, K., Gryak, J., Bishu, S., Rice, M. D., Waljee, A. K., Wilkins, H. J., & Stidham, R. W. (2021) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.08.011 · Cited by 118
Lynch, K. D., Chapman, R. W., Keshav, S., Montano-Loza, A. J., Mason, A. L., Kremer, A. E., Vetter, M., de Krijger, M., Ponsioen, C. Y., Trivedi, P., et al. (2020) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2019.05.013 · Cited by 105
Iacucci, M., Daperno, M., Lazarev, M., Arsenascu, R., Tontini, G. E., Akinola, O., Gui, X. S., Villanacci, V., Goetz, M., Lowerison, M., et al. (2017) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2017.03.012 · Cited by 92
Cannatelli, R., Parigi, T. L., Iacucci, M., Nardone, O. M., Tontini, G. E., Labarile, N., Buda, A., Rimondi, A., Bazarova, A., Bisschops, R., et al. (2023) · Endoscopy · doi:10.1055/a-1960-3645 · Cited by 80
Deep learning enabled classification of Mayo endoscopic subscore in patients with ulcerative colitis
Bhambhvani, H. P., & Zamora, A. (2021) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000001952 · Cited by 59
Fan, Y., Mu, R., Xu, H., Xie, C., Zhang, Y., Liu, L., Wang, L., Shi, H., Hu, Y., Ren, J., et al. (2023) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2022.08.015 · Cited by 58
Byrne, M. F., Panaccione, R., East, J. E., Iacucci, M., Parsa, N., Kalapala, R., Reddy, D. N., Ramesh Rughwani, H., Singh, A. P., Berry, S. K., et al. (2023) · Journal of Crohn's and Colitis · doi:10.1093/ecco-jcc/jjac152 · Cited by 51
Self-citationIacucci, M., Jeffery, L., Acharjee, A., Grisan, E., Buda, A., Nardone, O. M., Smith, S. C. L., Labarile, N., Zardo, D., Ungar, B., et al. (2023) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izac233 · Cited by 50
Ricciuto, A., Fish, J., Carman, N., Walters, T. D., Church, P. C., Hansen, B. E., Crowley, E., Siddiqui, I., Nguyen, G. C., Kamath, B. M., et al. (2018) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2018.01.020 · Cited by 47
Syed, S., & Stidham, R. W. (2020) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa211 · Cited by 16
Metadata
- Authors
- Travis, S. P. L., Schnell, D., Krzeski, P., Abreu, M. T., Altman, D. G., Colombel, J.-F., Feagan, B. G., Hanauer, S. B., Lémann, M., Lichtenstein, G. R., Marteau, P. R., Reinisch, W., Sands, B. E., Yacyshyn, B. R., Bernhardt, C. A., Mary, J.-Y., & Sandborn, W. J.
- Venue
- Gut
- Publisher
- BMJ
- Published
- 2011-10-13
- Volume
- 61
- Issue
- 4
- Pages
- 535-542
- ISSN
- 0017-5749 (ISSN-L)
- 1468-3288
- OpenAlex
- W2135328294
- Cited by
- 632
- References
- 1
- FWCI
- 13.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
