ParAlg: A Paraphasia Algorithm for Multinomial Classification of Picture Naming Errors.
Purpose: A preliminary version of a paraphasia classification algorithm (henceforth called ParAlg) has previously been shown to be a viable method for coding picture naming errors. The purpose of this study is to present an updated version of ParAlg, which uses multinomial classification, and compre...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 66; no. 3; pp. 966 - 987 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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American Speech-Language-Hearing Association
Mar2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=162305544&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 162305544 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Mar2023 vid: 66 iid: 3 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 162305544 10.1044/2022_JSLHR-22-00255 ppf: 966 ppct: 21 formats: fmt: @attributes: type: P size: 1.7MB tig: atl: ParAlg: A Paraphasia Algorithm for Multinomial Classification of Picture Naming Errors. aug: au: Casilio, Marianne Fergadiotis, Gerasimos Salem, Alexandra C. Gale, Robert C. McKinney-Bock, Katy Bedrick, Steven affil: Vanderbilt University Medical Center, Nashville, TN Portland State University, OR Oregon Health & Science University, Portland su: Semantics Cognition Documentation Phonetics Decision trees Statistics Vowels Phonological awareness Predictive tests Database management Benchmarking (Management) Inter-observer reliability Anomia Research funding Consonants Sensitivity & specificity (Statistics) Algorithms Medical coding sug: subj: Semantics Cognition Documentation Phonetics Data Processing, Hosting, and Related Services Decision trees Statistics Vowels Phonological awareness Predictive tests Database management Benchmarking (Management) Inter-observer reliability Anomia Research funding Consonants Sensitivity & specificity (Statistics) Algorithms Medical coding ab: Purpose: A preliminary version of a paraphasia classification algorithm (henceforth called ParAlg) has previously been shown to be a viable method for coding picture naming errors. The purpose of this study is to present an updated version of ParAlg, which uses multinomial classification, and comprehensively evaluate its performance when using two different forms of transcribed input. Method: A subset of 11,999 archival responses produced on the Philadelphia Naming Test were classified into six cardinal paraphasia types using ParAlg under two transcription configurations: (a) using phonemic transcriptions for responses exclusively (phonemic-only) and (b) using phonemic transcriptions for nonlexical responses and orthographic transcriptions for lexical responses (orthographic-lexical). Agreement was quantified by comparing ParAlg-generated paraphasia codes between configurations and relative to human-annotated codes using four metrics (positive predictive value, sensitivity, specificity, and F1 score). An item-level qualitative analysis of misclassifications under the best performing configuration was also completed to identify the source and nature of coding discrepancies. Results: Agreement between ParAlg-generated and human-annotated codes was high, although the orthographic-lexical configuration outperformed phonemic-only (weighted-average F1 scores of .78 and .87, respectively). A qualitative analysis of the orthographic-lexical configuration revealed a mix of human- and ParAlg-related misclassifications, the former of which were related primarily to phonological similarity judgments whereas the latter were due to semantic similarity assignment. Conclusions: ParAlg is an accurate and efficient alternative to manual scoring of paraphasias, particularly when lexical responses are orthographically transcribed. With further development, it has the potential to be a useful software application for anomia assessment. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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