{"id":"W4322491267","doi":"10.1044/2022_ajslp-22-00119","title":"Linear Mixed-Model Analysis Better Captures Subcomponents of Attention in a Small Sample Size of Persons With Aphasia","year":2023,"lang":"en","type":"article","venue":"American Journal of Speech-Language Pathology","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Psychology; Analysis of variance; Nonparametric statistics; Sample size determination; Repeated measures design; Aphasia; Mixed-design analysis of variance; Cognitive psychology; Audiology; Statistics; Mathematics; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02468085,0.001605058,0.002128662,0.001714864,0.000820717,0.001743851,0.001106853,0.0008530965,0.003293687],"category_scores_gemma":[0.06601088,0.0004860656,0.002327628,0.001131675,0.0006448446,0.001507236,0.0009938612,0.001323703,0.000364422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005465405,"about_ca_system_score_gemma":0.001196102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003836963,"about_ca_topic_score_gemma":0.005564049,"domain_scores_codex":[0.9828956,0.01265747,0.0006973824,0.002737366,0.0007476857,0.0002645204],"domain_scores_gemma":[0.9550558,0.03736813,0.002054581,0.003876278,0.001327098,0.000318133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007723808,0.001701737,0.7025409,0.001273383,0.01426242,0.001036772,0.002666093,0.01739977,0.00858108,0.005726339,0.005563934,0.2315237],"study_design_scores_gemma":[0.0009212258,0.01212243,0.5153423,0.0004871065,0.005314161,0.001167595,0.002022168,0.4189228,0.006238764,0.02565125,0.01159324,0.0002170001],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6173367,0.001627906,0.3742085,0.0006771118,0.0002932537,0.001344528,0.001374588,0.001118944,0.002018508],"genre_scores_gemma":[0.9359059,0.0001567048,0.06010755,0.0002134063,0.00009111095,0.001680874,0.001021505,0.000145737,0.000677228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02468085,"threshold_uncertainty_score":0.1305264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047093564484598,"score_gpt":0.2861075521504397,"score_spread":0.2656366165055937,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}