{"id":"W4212820910","doi":"10.2196/35803","title":"Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Attention Deficit Hyperactivity Disorder","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health","keywords":"Attention deficit hyperactivity disorder; Smartwatch; Wearable computer; Presentation (obstetrics); Usability; Motion sensors; Smartphone application; Artificial intelligence; Machine learning; Psychology; Computer science; Medicine; Audiology; Human–computer interaction; Multimedia; Clinical psychology; Embedded system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003367261,0.0001500931,0.000389373,0.0004953916,0.0008195852,0.00002581091,0.00007340805,0.00002105715,0.0001959311],"category_scores_gemma":[0.0003184481,0.0001413433,0.00005215781,0.0007615276,0.0001560723,0.0002778052,0.0005001563,0.00140628,0.000007244787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006196124,"about_ca_system_score_gemma":0.000231019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000829984,"about_ca_topic_score_gemma":0.00005261618,"domain_scores_codex":[0.9953242,0.001602186,0.000296151,0.0003016615,0.0021113,0.0003645271],"domain_scores_gemma":[0.9983758,0.0003150743,0.0001529854,0.0002445642,0.00080579,0.0001058303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006097765,0.02548168,0.4904648,0.0006590574,0.001073248,0.0001608684,0.05517344,0.00259442,0.3837256,0.00004811352,0.0001093973,0.03441159],"study_design_scores_gemma":[0.01008734,0.04988988,0.5321652,0.0001627779,0.0001663511,0.0005478513,0.3387728,0.06219201,0.004329233,0.00007756433,0.001058279,0.000550717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965635,0.0002257778,0.0002888984,0.00008065505,0.00004688959,0.001987517,0.00001338704,0.00003332528,0.0007600391],"genre_scores_gemma":[0.9996145,0.000009800628,0.00006871265,0.000008717113,0.00001885376,0.0001282782,0.000007089494,0.00002926808,0.0001147479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3793963,"threshold_uncertainty_score":0.630367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2254180089790529,"score_gpt":0.4437963173997669,"score_spread":0.218378308420714,"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."}}