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A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists

OBJECTIVE: To compare techniques for computing clustering and switching scores in terms of agreement, correlation, and empirical value as predictors of incident cognitive impairment (ICI). METHODS: We transcribed animal and letter F fluency recordings on 640 cases of ICI and matched controls from a...

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Autores principales: Bushnell, Justin, Svaldi, Diana, Ayers, Matthew R., Gao, Sujuan, Unverzagt, Frederick, Gaizo, John Del, Wadley, Virginia G., Kennedy, Richard, Goñi, Joaquín, Clark, David Glenn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9518694/
https://www.ncbi.nlm.nih.gov/pubmed/36186334
http://dx.doi.org/10.3389/fpsyg.2022.743557
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author Bushnell, Justin
Svaldi, Diana
Ayers, Matthew R.
Gao, Sujuan
Unverzagt, Frederick
Gaizo, John Del
Wadley, Virginia G.
Kennedy, Richard
Goñi, Joaquín
Clark, David Glenn
author_facet Bushnell, Justin
Svaldi, Diana
Ayers, Matthew R.
Gao, Sujuan
Unverzagt, Frederick
Gaizo, John Del
Wadley, Virginia G.
Kennedy, Richard
Goñi, Joaquín
Clark, David Glenn
author_sort Bushnell, Justin
collection PubMed
description OBJECTIVE: To compare techniques for computing clustering and switching scores in terms of agreement, correlation, and empirical value as predictors of incident cognitive impairment (ICI). METHODS: We transcribed animal and letter F fluency recordings on 640 cases of ICI and matched controls from a national epidemiological study, amending each transcription with word timings. We then calculated clustering and switching scores, as well as scores indexing speed of responses, using techniques described in the literature. We evaluated agreement among the techniques with Cohen’s κ and calculated correlations among the scores. After fitting a base model with raw scores, repetitions, and intrusions, we fit a series of Bayesian logistic regression models adding either clustering and switching scores or speed scores, comparing the models in terms of several metrics. We partitioned the ICI cases into acute and progressive cases and repeated the regression analysis for each group. RESULTS: For animal fluency, we found that models with speed scores derived using the slope difference algorithm achieved the best values of the Watanabe–Akaike Information Criterion (WAIC), but with good net reclassification improvement (NRI) only for the progressive group (8.2%). For letter fluency, different models excelled for prediction of acute and progressive cases. For acute cases, NRI was best for speed scores derived from a network model (3.4%), while for progressive cases, the best model used clustering and switching scores derived from the same network model (5.1%). Combining variables from the best animal and letter F models led to marginal improvements in model fit and NRI only for the all-cases and acute-cases analyses. CONCLUSION: Speed scores improve a base model for predicting progressive cognitive impairment from animal fluency. Letter fluency scores may provide complementary information.
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spelling pubmed-95186942022-09-29 A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists Bushnell, Justin Svaldi, Diana Ayers, Matthew R. Gao, Sujuan Unverzagt, Frederick Gaizo, John Del Wadley, Virginia G. Kennedy, Richard Goñi, Joaquín Clark, David Glenn Front Psychol Psychology OBJECTIVE: To compare techniques for computing clustering and switching scores in terms of agreement, correlation, and empirical value as predictors of incident cognitive impairment (ICI). METHODS: We transcribed animal and letter F fluency recordings on 640 cases of ICI and matched controls from a national epidemiological study, amending each transcription with word timings. We then calculated clustering and switching scores, as well as scores indexing speed of responses, using techniques described in the literature. We evaluated agreement among the techniques with Cohen’s κ and calculated correlations among the scores. After fitting a base model with raw scores, repetitions, and intrusions, we fit a series of Bayesian logistic regression models adding either clustering and switching scores or speed scores, comparing the models in terms of several metrics. We partitioned the ICI cases into acute and progressive cases and repeated the regression analysis for each group. RESULTS: For animal fluency, we found that models with speed scores derived using the slope difference algorithm achieved the best values of the Watanabe–Akaike Information Criterion (WAIC), but with good net reclassification improvement (NRI) only for the progressive group (8.2%). For letter fluency, different models excelled for prediction of acute and progressive cases. For acute cases, NRI was best for speed scores derived from a network model (3.4%), while for progressive cases, the best model used clustering and switching scores derived from the same network model (5.1%). Combining variables from the best animal and letter F models led to marginal improvements in model fit and NRI only for the all-cases and acute-cases analyses. CONCLUSION: Speed scores improve a base model for predicting progressive cognitive impairment from animal fluency. Letter fluency scores may provide complementary information. Frontiers Media S.A. 2022-09-14 /pmc/articles/PMC9518694/ /pubmed/36186334 http://dx.doi.org/10.3389/fpsyg.2022.743557 Text en Copyright © 2022 Bushnell, Svaldi, Ayers, Gao, Unverzagt, Del Gaizo, Wadley, Kennedy, Goñi and Clark. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Bushnell, Justin
Svaldi, Diana
Ayers, Matthew R.
Gao, Sujuan
Unverzagt, Frederick
Gaizo, John Del
Wadley, Virginia G.
Kennedy, Richard
Goñi, Joaquín
Clark, David Glenn
A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists
title A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists
title_full A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists
title_fullStr A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists
title_full_unstemmed A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists
title_short A comparison of techniques for deriving clustering and switching scores from verbal fluency word lists
title_sort comparison of techniques for deriving clustering and switching scores from verbal fluency word lists
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9518694/
https://www.ncbi.nlm.nih.gov/pubmed/36186334
http://dx.doi.org/10.3389/fpsyg.2022.743557
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