{"id":"W2074211783","doi":"10.1016/j.jneumeth.2005.02.007","title":"Automatic detection of movement disorders using recordings of rapid alternating movements","year":2005,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Institut Universitaire de Gériatrie de Montréal; École de Technologie Supérieure","funders":"","keywords":"Jerk; Movement disorders; Movement (music); Statistical analysis; Pattern recognition (psychology); Population; Artificial intelligence; Mathematics; Psychology; Physical medicine and rehabilitation; Statistics; Computer science; Medicine; Physics; Disease; Pathology; Acoustics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002083125,0.0001481168,0.0003492832,0.0003945975,0.0001102609,0.00004850121,0.0006499224,0.00003326045,0.00001474495],"category_scores_gemma":[0.001560019,0.0001218398,0.0001731809,0.0006060017,0.0001936765,0.0006797196,0.0001379659,0.0002221062,2.785653e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006520243,"about_ca_system_score_gemma":0.00005176342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001442715,"about_ca_topic_score_gemma":0.000001174398,"domain_scores_codex":[0.9972431,0.0005518518,0.001026275,0.0002571213,0.0006623552,0.0002593106],"domain_scores_gemma":[0.9974394,0.0004833538,0.001703167,0.0001869458,0.0001000237,0.00008709424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000107414,0.00009234581,0.0001812976,0.00002394767,0.000002195565,0.000001500846,0.000253516,0.003460926,0.7452721,0.00001019106,0.000002364126,0.2506889],"study_design_scores_gemma":[0.0002565069,0.0004933652,0.001261604,0.00009199332,0.000009613761,0.00002964204,0.00006077797,0.2322766,0.764778,0.0004701438,0.000195669,0.00007609012],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.807035,0.00003431086,0.1914332,0.0001134233,0.001176528,0.00009604779,0.000001329819,0.00001016995,0.00009996902],"genre_scores_gemma":[0.8740869,0.00005744133,0.1251811,0.0005644985,0.00007406784,7.290943e-7,9.146273e-9,0.00001125589,0.00002399251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2506128,"threshold_uncertainty_score":0.4968481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07409294327984656,"score_gpt":0.3916724141510436,"score_spread":0.3175794708711971,"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."}}