{"id":"W3083828561","doi":"10.1371/journal.pone.0247574","title":"Fall risk classification for people with lower extremity amputations using random forests and smartphone sensor features from a 6-minute walk test","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Ottawa Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Random forest; Feature selection; Computer science; Accelerometer; Artificial intelligence; Classifier (UML); Decision tree learning; Physical medicine and rehabilitation; Decision tree; Medicine; Machine learning","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":[0.0006822147,0.0006294067,0.000587895,0.001796577,0.000228897,0.00040052,0.0002778484,0.0004071677,0.0006970014],"category_scores_gemma":[0.003044141,0.0001437452,0.001039522,0.0005529238,0.0000989606,0.0003230451,0.0003380513,0.0003718229,0.0003170031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001988971,"about_ca_system_score_gemma":0.0002661486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007866689,"about_ca_topic_score_gemma":0.01044378,"domain_scores_codex":[0.9996693,0.00005998292,0.00004658399,0.0000841313,0.00007926547,0.00006074869],"domain_scores_gemma":[0.99932,0.0002529407,0.0001424577,0.00003989063,0.0001779349,0.00006671077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001388116,0.0006025839,0.801087,0.0001551708,0.0002978504,0.0006333696,0.0002574345,0.02514545,0.00453968,0.0001350247,0.002546918,0.1632114],"study_design_scores_gemma":[0.0000444444,0.0006482325,0.6204047,0.00008463896,0.0001730568,0.0007805155,0.0005397177,0.3742093,0.001889908,0.0006685542,0.000503455,0.00005327067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985835,0.0002080587,0.01201167,0.0000929948,0.00002516105,0.0000668185,0.00127353,0.0001602232,0.0003264516],"genre_scores_gemma":[0.9930916,0.00007527869,0.005359376,0.0000153616,0.00001238971,0.00004432772,0.001233488,0.000005847882,0.0001623421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007866689,"threshold_uncertainty_score":0.01564181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03738968119619415,"score_gpt":0.2596464245129663,"score_spread":0.2222567433167721,"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."}}