FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3154545187> ?p ?o ?g. }

• W3154545187 endingPage "151" @default.
• W3154545187 startingPage "142" @default.
• W3154545187 abstract "Purpose To determine classification criteria for tubercular uveitis. Design Machine learning of cases with tubercular uveitis and 14 other uveitides. Methods Cases of noninfectious posterior uveitis or panuveitis, and of infectious posterior uveitis or panuveitis, were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation sets. Results Two hundred seventy-seven cases of tubercular uveitis were evaluated by machine learning against other uveitides. Key criteria for tubercular uveitis were a compatible uveitic syndrome, including (1) anterior uveitis with iris nodules, (2) serpiginous-like tubercular choroiditis, (3) choroidal nodule (tuberculoma), (4) occlusive retinal vasculitis, and (5) in hosts with evidence of active systemic tuberculosis, multifocal choroiditis; and evidence of tuberculosis, including histologically or microbiologically confirmed infection, positive interferon-γ release assay test, or positive tuberculin skin test. The overall accuracy of the diagnosis of tubercular uveitis vs other uveitides in the validation set was 98.2% (95% confidence interval 96.5, 99.1). The misclassification rates for tubercular uveitis were training set, 3.4%; and validation set, 3.6%. Conclusions The criteria for tubercular uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research. To determine classification criteria for tubercular uveitis. Machine learning of cases with tubercular uveitis and 14 other uveitides. Cases of noninfectious posterior uveitis or panuveitis, and of infectious posterior uveitis or panuveitis, were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were analyzed by anatomic class, and each class was split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated on the validation sets. Two hundred seventy-seven cases of tubercular uveitis were evaluated by machine learning against other uveitides. Key criteria for tubercular uveitis were a compatible uveitic syndrome, including (1) anterior uveitis with iris nodules, (2) serpiginous-like tubercular choroiditis, (3) choroidal nodule (tuberculoma), (4) occlusive retinal vasculitis, and (5) in hosts with evidence of active systemic tuberculosis, multifocal choroiditis; and evidence of tuberculosis, including histologically or microbiologically confirmed infection, positive interferon-γ release assay test, or positive tuberculin skin test. The overall accuracy of the diagnosis of tubercular uveitis vs other uveitides in the validation set was 98.2% (95% confidence interval 96.5, 99.1). The misclassification rates for tubercular uveitis were training set, 3.4%; and validation set, 3.6%. The criteria for tubercular uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research." @default.
• W3154545187 created "2021-04-26" @default.
• W3154545187 creator A5007874257 @default.
• W3154545187 creator A5015064769 @default.
• W3154545187 creator A5022183607 @default.
• W3154545187 creator A5022308888 @default.
• W3154545187 creator A5030834390 @default.
• W3154545187 creator A5035832206 @default.
• W3154545187 creator A5055467340 @default.
• W3154545187 creator A5062196360 @default.
• W3154545187 creator A5079059345 @default.
• W3154545187 creator A5082563606 @default.
• W3154545187 creator A5086216178 @default.
• W3154545187 creator A5086984489 @default.
• W3154545187 date "2021-08-01" @default.
• W3154545187 modified "2023-10-16" @default.
• W3154545187 title "Classification Criteria for Tubercular Uveitis" @default.
• W3154545187 cites W1575205563 @default.
• W3154545187 cites W1975128690 @default.
• W3154545187 cites W1994689842 @default.
• W3154545187 cites W2001927204 @default.
• W3154545187 cites W2006687015 @default.
• W3154545187 cites W2034534130 @default.
• W3154545187 cites W2051160789 @default.
• W3154545187 cites W2108825865 @default.
• W3154545187 cites W2116012373 @default.
• W3154545187 cites W2127681731 @default.
• W3154545187 cites W2139499737 @default.
• W3154545187 cites W2142340713 @default.
• W3154545187 cites W2145360720 @default.
• W3154545187 cites W2150160012 @default.
• W3154545187 cites W2164625752 @default.
• W3154545187 cites W2767182178 @default.
• W3154545187 cites W2767283988 @default.
• W3154545187 cites W2794754833 @default.
• W3154545187 cites W2801789369 @default.
• W3154545187 cites W2804195106 @default.
• W3154545187 cites W2888931815 @default.
• W3154545187 cites W2915511073 @default.
• W3154545187 cites W3155612688 @default.
• W3154545187 doi "https://doi.org/10.1016/j.ajo.2021.03.040" @default.
• W3154545187 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33845014" @default.
• W3154545187 hasPublicationYear "2021" @default.
• W3154545187 type Work @default.
• W3154545187 sameAs 3154545187 @default.
• W3154545187 citedByCount "27" @default.
• W3154545187 countsByYear W31545451872021 @default.
• W3154545187 countsByYear W31545451872022 @default.
• W3154545187 countsByYear W31545451872023 @default.
• W3154545187 crossrefType "journal-article" @default.
• W3154545187 hasAuthorship W3154545187A5007874257 @default.
• W3154545187 hasAuthorship W3154545187A5015064769 @default.
• W3154545187 hasAuthorship W3154545187A5022183607 @default.
• W3154545187 hasAuthorship W3154545187A5022308888 @default.
• W3154545187 hasAuthorship W3154545187A5030834390 @default.
• W3154545187 hasAuthorship W3154545187A5035832206 @default.
• W3154545187 hasAuthorship W3154545187A5055467340 @default.
• W3154545187 hasAuthorship W3154545187A5062196360 @default.
• W3154545187 hasAuthorship W3154545187A5079059345 @default.
• W3154545187 hasAuthorship W3154545187A5082563606 @default.
• W3154545187 hasAuthorship W3154545187A5086216178 @default.
• W3154545187 hasAuthorship W3154545187A5086984489 @default.
• W3154545187 hasBestOaLocation W31545451872 @default.
• W3154545187 hasConcept C118487528 @default.
• W3154545187 hasConcept C126322002 @default.
• W3154545187 hasConcept C142724271 @default.
• W3154545187 hasConcept C151956035 @default.
• W3154545187 hasConcept C154945302 @default.
• W3154545187 hasConcept C16005928 @default.
• W3154545187 hasConcept C2776194053 @default.
• W3154545187 hasConcept C2776247216 @default.
• W3154545187 hasConcept C2779368723 @default.
• W3154545187 hasConcept C2781069245 @default.
• W3154545187 hasConcept C2911002200 @default.
• W3154545187 hasConcept C2992250680 @default.
• W3154545187 hasConcept C41008148 @default.
• W3154545187 hasConcept C44249647 @default.
• W3154545187 hasConcept C71924100 @default.
• W3154545187 hasConceptScore W3154545187C118487528 @default.
• W3154545187 hasConceptScore W3154545187C126322002 @default.
• W3154545187 hasConceptScore W3154545187C142724271 @default.
• W3154545187 hasConceptScore W3154545187C151956035 @default.
• W3154545187 hasConceptScore W3154545187C154945302 @default.
• W3154545187 hasConceptScore W3154545187C16005928 @default.
• W3154545187 hasConceptScore W3154545187C2776194053 @default.
• W3154545187 hasConceptScore W3154545187C2776247216 @default.
• W3154545187 hasConceptScore W3154545187C2779368723 @default.
• W3154545187 hasConceptScore W3154545187C2781069245 @default.
• W3154545187 hasConceptScore W3154545187C2911002200 @default.
• W3154545187 hasConceptScore W3154545187C2992250680 @default.
• W3154545187 hasConceptScore W3154545187C41008148 @default.
• W3154545187 hasConceptScore W3154545187C44249647 @default.
• W3154545187 hasConceptScore W3154545187C71924100 @default.
• W3154545187 hasFunder F4320337350 @default.
• W3154545187 hasLocation W31545451871 @default.
• W3154545187 hasLocation W31545451872 @default.
• W3154545187 hasLocation W31545451873 @default.
• W3154545187 hasOpenAccess W3154545187 @default.

• next