{"id":"W4390080688","doi":"10.1177/20552076231219102","title":"Validation of automated pipeline for the assessment of a motor speech disorder in amyotrophic lateral sclerosis (ALS)","year":2023,"lang":"en","type":"article","venue":"Digital Health","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; Toronto Rehabilitation Institute; University of Toronto; Health Sciences Centre; University Health Network","funders":"National Institute on Deafness and Other Communication Disorders; National Institutes of Health; Mitacs","keywords":"Univariate; Multivariate statistics; Multivariate analysis; Amyotrophic lateral sclerosis; Spearman's rank correlation coefficient; Pipeline (software); Computer science; Linear discriminant analysis; Missing data; Correlation; Canonical correlation; Artificial intelligence; Pattern recognition (psychology); Mathematics; Medicine; Machine learning; Disease; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005132592,0.001077889,0.000607949,0.001260965,0.0005545851,0.0009805551,0.0007932977,0.0007166745,0.001784822],"category_scores_gemma":[0.01069319,0.0003806119,0.0007214645,0.0005067371,0.00061522,0.0007116761,0.001411228,0.0006282547,0.001534699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006363234,"about_ca_system_score_gemma":0.001649297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003629233,"about_ca_topic_score_gemma":0.004579897,"domain_scores_codex":[0.9968973,0.0008562626,0.0002678698,0.001021877,0.0007342227,0.0002224224],"domain_scores_gemma":[0.9941483,0.002363226,0.0003952421,0.0006303832,0.002262517,0.0002004012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003293441,0.0008954746,0.1294513,0.000556636,0.0005071181,0.000529386,0.001275389,0.02785653,0.2925563,0.0009927165,0.009230762,0.5328549],"study_design_scores_gemma":[0.0002843803,0.001728698,0.226397,0.0001165326,0.0002560151,0.001149326,0.0004477763,0.5591658,0.2013835,0.001645144,0.007249764,0.0001759336],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6368445,0.000330594,0.3442676,0.0002540039,0.0001304892,0.0007126646,0.003526744,0.0123832,0.001550101],"genre_scores_gemma":[0.7613964,0.00009223955,0.2311257,0.0001459018,0.00004009844,0.0008628514,0.004907082,0.0004425427,0.0009871508],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005132592,"threshold_uncertainty_score":0.02714401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04277377356097081,"score_gpt":0.3634888867789245,"score_spread":0.3207151132179537,"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."}}