{"id":"W2015222561","doi":"10.1186/1472-6947-7-41","title":"Using machine learning algorithms to guide rehabilitation planning for home care clients","year":2007,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; Homewood Research Institute; University of Waterloo","funders":"Institute of Musculoskeletal Health and Arthritis; Canadian Institutes of Health Research","keywords":"Machine learning; Support vector machine; Rehabilitation; Algorithm; Artificial intelligence; Protocol (science); Health informatics; Computer science; Clinical decision support system; Activities of daily living; Health care; Medicine; Decision support system; Nursing; Physical therapy; Public health; Alternative medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001766517,0.0001461665,0.0003266187,0.0004251545,0.0002079745,0.0000507533,0.00007592651,0.0001442638,0.0000354382],"category_scores_gemma":[0.005418368,0.0001139716,0.0001257848,0.0002425981,0.00005213344,0.0001354843,0.00009261807,0.0002111895,0.000007263824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001355557,"about_ca_system_score_gemma":0.0001508484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000588198,"about_ca_topic_score_gemma":0.000009575569,"domain_scores_codex":[0.9977565,0.00002117578,0.0009555395,0.0001591298,0.0008098488,0.0002978081],"domain_scores_gemma":[0.994297,0.004745875,0.0001662294,0.0001531846,0.0002669532,0.0003707592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007041097,0.00004322189,0.1609989,0.0009397779,0.00002163881,0.000007317109,0.00727355,0.002805194,0.00003673217,0.0001721569,0.000264942,0.8267324],"study_design_scores_gemma":[0.00362892,0.001140981,0.0296057,0.003588523,0.00005679981,0.0001067025,0.02404427,0.9193549,0.000029151,0.0003907296,0.01776672,0.0002866149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4773914,0.0002048152,0.5214954,0.000009418524,0.0002584071,0.0003158194,0.000003292318,0.0000271999,0.0002942428],"genre_scores_gemma":[0.2323224,0.0000122219,0.7669345,0.0005089692,0.0001531601,0.000007910687,0.00001981199,0.00001556341,0.00002550133],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9165497,"threshold_uncertainty_score":0.6486684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04535450033323893,"score_gpt":0.4047281305561135,"score_spread":0.3593736302228745,"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."}}