{"id":"W2800532564","doi":"10.2196/rehab.8335","title":"A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease: Validation Study Under Real Conditions of Use","year":2018,"lang":"en","type":"article","venue":"JMIR Rehabilitation and Assistive Technologies","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Regional Development Fund; Instituto de Salud Carlos III","keywords":"Kinematics; Parkinson's disease; Algorithm; Dyskinesia; Gold standard (test); Gait; Physical medicine and rehabilitation; Computer science; Medicine; Disease; Internal medicine","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":[0.005319736,0.0009519513,0.0007436154,0.001390038,0.0002646821,0.0007421156,0.000947179,0.0009655724,0.0006668065],"category_scores_gemma":[0.01409088,0.0002302798,0.0005598662,0.0008000688,0.0004255228,0.0006259477,0.0005963214,0.0004268891,0.0003388987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004681633,"about_ca_system_score_gemma":0.0006776801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002342934,"about_ca_topic_score_gemma":0.001497857,"domain_scores_codex":[0.9968352,0.001046039,0.0004594261,0.0004933917,0.001036442,0.0001296216],"domain_scores_gemma":[0.990783,0.004225482,0.0008645228,0.0007670475,0.003185187,0.0001746485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004319011,0.002394749,0.5091062,0.0006991085,0.0009524828,0.0004120266,0.0004799389,0.05230528,0.03416741,0.0005439693,0.001472241,0.3931477],"study_design_scores_gemma":[0.0006666252,0.01265686,0.3937021,0.0002051,0.0006715429,0.003290646,0.0002894992,0.5357688,0.04850868,0.0004335456,0.003665364,0.0001412791],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8871317,0.0005585477,0.1091134,0.0000988186,0.00009266219,0.000812242,0.0005453299,0.0007027914,0.0009445227],"genre_scores_gemma":[0.9122885,0.000194485,0.08537966,0.00005523892,0.00002578066,0.000490524,0.001110547,0.00004362338,0.0004116333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005319736,"threshold_uncertainty_score":0.02813375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02959068833187024,"score_gpt":0.3820360197097793,"score_spread":0.3524453313779091,"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."}}