{"id":"W4388014509","doi":"10.2196/47167","title":"Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; European Commission; Ministerio de Ciencia, Innovación y Universidades; Comunidad de Madrid","keywords":"Artificial intelligence; Machine learning; Computer science; Feature (linguistics); Domain (mathematical analysis); Medicine; Statistics; Physical medicine and rehabilitation; Mathematics","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":[0.003286935,0.0006756319,0.0004909397,0.0005257419,0.0002164906,0.0005515881,0.0006394242,0.0008395443,0.0006824648],"category_scores_gemma":[0.006471726,0.0001734277,0.000600218,0.0004640078,0.0005028316,0.0005565165,0.0005594893,0.0005947214,0.0003562025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003765623,"about_ca_system_score_gemma":0.0007281278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002783043,"about_ca_topic_score_gemma":0.00244031,"domain_scores_codex":[0.9989285,0.0005143609,0.00007194914,0.0001857433,0.0001959471,0.0001036266],"domain_scores_gemma":[0.9948899,0.002744916,0.0004295444,0.00045732,0.001036193,0.0004420773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007009754,0.01935938,0.6476636,0.001164425,0.0009453954,0.003792196,0.002029671,0.08950139,0.03692441,0.002157883,0.007290937,0.1821609],"study_design_scores_gemma":[0.0008427344,0.01791552,0.3784606,0.0001426436,0.0003276675,0.002082705,0.00251296,0.5801159,0.01171576,0.001969467,0.003737035,0.0001769711],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986895,0.00006852286,0.01151167,0.00014132,0.00002389531,0.0003943669,0.0006111276,0.00006665601,0.0002875828],"genre_scores_gemma":[0.9834893,0.00006678919,0.01448304,0.00007820909,0.00002694624,0.0005391047,0.001126489,0.000008958011,0.0001812702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003286935,"threshold_uncertainty_score":0.01738316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1757622148095712,"score_gpt":0.5435575536962893,"score_spread":0.3677953388867181,"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."}}