{"id":"W4213417251","doi":"10.3390/s22051749","title":"Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Smartphone application; Computer science; Artificial intelligence; Machine learning; Human–computer interaction; Physical medicine and rehabilitation; Simulation; Embedded system; Medicine; Multimedia","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003574111,0.0006297397,0.0005392444,0.001345071,0.0001488635,0.000493858,0.0003476327,0.0005724737,0.0008536441],"category_scores_gemma":[0.002215338,0.000150477,0.0005697349,0.0007294592,0.000125284,0.0004160106,0.0004548132,0.0003940619,0.0006698712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000287059,"about_ca_system_score_gemma":0.000301593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006241132,"about_ca_topic_score_gemma":0.008440194,"domain_scores_codex":[0.9997558,0.0000332551,0.00002644071,0.00008494013,0.00006245664,0.0000371042],"domain_scores_gemma":[0.9995061,0.0001484757,0.00008306592,0.00004030066,0.0001752178,0.00004684116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001557653,0.001167116,0.3485378,0.0004274706,0.0004628501,0.001613419,0.0003810711,0.08765713,0.01087631,0.0003978372,0.008826644,0.5380947],"study_design_scores_gemma":[0.00004159056,0.0006778048,0.2708504,0.0001298066,0.0001107982,0.0008171108,0.000405684,0.7199212,0.003883159,0.001008561,0.002098107,0.00005582815],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712326,0.0008783349,0.0222234,0.0003132992,0.0001101958,0.0001283218,0.003056438,0.0005054818,0.001551906],"genre_scores_gemma":[0.9864078,0.0003552381,0.008635845,0.00009020709,0.00004775244,0.00009667845,0.003186547,0.00001023321,0.001169732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006241132,"threshold_uncertainty_score":0.01240963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07985555337996017,"score_gpt":0.3612671624545941,"score_spread":0.2814116090746339,"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."}}