{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002992748,0.0008943668,0.0005007562,0.0008116559,0.0005576993,0.0003929658,0.0006305057,0.0007065261,0.003086116],"category_scores_gemma":[0.0039432,0.000371983,0.0004868848,0.0002867365,0.0006824541,0.0006602593,0.0007112934,0.0007145031,0.0006716842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003691032,"about_ca_system_score_gemma":0.0006892505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003527016,"about_ca_topic_score_gemma":0.00606118,"domain_scores_codex":[0.9983682,0.0008529642,0.0001496363,0.0001928553,0.0002675524,0.0001688549],"domain_scores_gemma":[0.9963977,0.00158612,0.0004324016,0.0002417325,0.0008290404,0.0005129849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009390302,0.1005032,0.7121957,0.001188911,0.000412071,0.002405822,0.007952288,0.0009476933,0.03093014,0.0002062407,0.001288539,0.1325791],"study_design_scores_gemma":[0.002920215,0.3113947,0.6635426,0.0001039374,0.0003118241,0.002277307,0.004369185,0.003130859,0.009917724,0.0001616848,0.001800944,0.00006905186],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973646,0.00007206809,0.0008055863,0.00003674551,0.000008934925,0.0009639919,0.0001870213,0.00002316496,0.0005379208],"genre_scores_gemma":[0.9920846,0.000209171,0.004561487,0.0001306189,0.00002592134,0.001722957,0.0003721662,0.00001015446,0.0008829641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003527016,"threshold_uncertainty_score":0.01582736,"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."}}