{"id":"W155410030","doi":"","title":"Predicting rehabilitation potential with the K -nearest neighbors algorithm: a comparison with the current clinical assessment protocol","year":2006,"lang":"en","type":"article","venue":"international conference on Modelling and simulation","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"k-nearest neighbors algorithm; Protocol (science); Computer science; Rehabilitation; Data mining; Machine learning; Current (fluid); Algorithm; Artificial intelligence; Health care; Medicine; Physical therapy; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.007623591,0.0003571682,0.0006788747,0.00142449,0.000746061,0.001147681,0.001043844,0.0006930326,0.0009389342],"category_scores_gemma":[0.04371656,0.0001337336,0.0003476889,0.001440742,0.000649187,0.001008739,0.0005886435,0.0004697227,0.0003782165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002692099,"about_ca_system_score_gemma":0.003603004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1271377,"about_ca_topic_score_gemma":0.1872008,"domain_scores_codex":[0.994397,0.002444851,0.0007296957,0.0006097984,0.00167795,0.0001405821],"domain_scores_gemma":[0.9751402,0.01347512,0.001730702,0.00126111,0.008029733,0.0003633018],"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":[0.001323861,0.0005943363,0.6986871,0.0003854104,0.0002383747,0.0002483132,0.0009379297,0.05692489,0.001083754,0.001010984,0.00484551,0.2337196],"study_design_scores_gemma":[0.0002189774,0.001148534,0.4085803,0.0001527978,0.0001599162,0.0004586789,0.001668206,0.5784871,0.002421224,0.002170139,0.004405821,0.0001283428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9504703,0.0005388191,0.03908918,0.0005918001,0.00007912333,0.001136993,0.002413991,0.0003198693,0.005359983],"genre_scores_gemma":[0.9740002,0.0001447027,0.02350169,0.00005579105,0.00001219113,0.0003972747,0.001331107,0.0000145257,0.0005426188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1271377,"threshold_uncertainty_score":0.2527954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09916232887340565,"score_gpt":0.4796244208802093,"score_spread":0.3804620920068037,"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."}}