{"id":"W4401824487","doi":"10.1002/mds.29987","title":"Automated Sleep Detection in Movement Disorders Using Deep Brain Stimulation and Machine Learning","year":2024,"lang":"en","type":"article","venue":"Movement Disorders","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; Krembil Foundation; University of Toronto","funders":"Genome Alberta; Genome British Columbia; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Ontario Genomics Institute; Boston Scientific Corporation","keywords":"Deep brain stimulation; Movement disorders; Sleep (system call); Neuroscience; Physical medicine and rehabilitation; Movement (music); Psychology; Stimulation; Medicine; Artificial intelligence; Computer science; Parkinson's disease; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001627153,0.0002643776,0.0002504424,0.0003013286,0.0001350454,0.00006177741,0.00004065096,0.0000978602,0.0001110748],"category_scores_gemma":[0.00005711524,0.0002313512,0.00008541423,0.0004385179,0.00005141873,0.0001465904,0.00005881123,0.0002603139,0.000009816995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001608426,"about_ca_system_score_gemma":0.00001512763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009361034,"about_ca_topic_score_gemma":0.002082372,"domain_scores_codex":[0.9984656,0.00008171478,0.0003239315,0.0005016424,0.0002717185,0.0003553692],"domain_scores_gemma":[0.9996263,0.00008196382,0.00005188151,0.000132962,0.00001214417,0.00009477734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004933433,0.001332309,0.5615768,0.0004342617,0.0003722369,0.00009304407,0.001025718,0.04945227,0.01505664,0.000369684,0.0001495563,0.3696441],"study_design_scores_gemma":[0.002444869,0.0007095432,0.1233846,0.00006842994,0.00008270265,2.073723e-7,0.0002231255,0.8439053,0.0001911176,0.02860361,0.0001787424,0.0002077185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859141,0.002005913,0.005959675,0.003968203,0.0001689428,0.0009639544,0.000004914747,0.0005036866,0.0005105725],"genre_scores_gemma":[0.9950392,0.000392504,0.0001693886,0.004104687,0.0000153124,0.00005760283,0.00005622161,0.00004366815,0.0001214499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7944531,"threshold_uncertainty_score":0.9434226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01340233617360116,"score_gpt":0.2709826510332841,"score_spread":0.2575803148596829,"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."}}