{"id":"W4405166321","doi":"10.1007/s40846-024-00922-3","title":"A Smart Recommender System for Stroke Risk Assessment with an Integrated Strokebot","year":2024,"lang":"en","type":"article","venue":"Journal of Medical and Biological Engineering","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Recommender system; Computer science; Classifier (UML); Upload; Risk assessment; User Friendly; Chatbot; Machine learning; Artificial intelligence; Risk analysis (engineering); World Wide Web; Medicine; Computer security","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001112216,0.0001476225,0.0003753033,0.0001110731,0.00002554772,0.00004057267,0.0001069257,0.0001545377,0.00005163077],"category_scores_gemma":[0.000171903,0.00007041831,0.00009687663,0.00009222821,0.00003831652,0.00006064243,0.00004172995,0.0006350757,5.332125e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001009408,"about_ca_system_score_gemma":0.00008114903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004799995,"about_ca_topic_score_gemma":4.840671e-7,"domain_scores_codex":[0.9988137,0.0000286486,0.0003739622,0.0001662454,0.0004195927,0.0001978177],"domain_scores_gemma":[0.9991924,0.0002237382,0.00007661858,0.00007398865,0.00007960451,0.0003536263],"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":[0.004918421,0.001494055,0.04132446,0.006995323,0.009647716,0.009671679,0.0004242593,0.0008528488,0.01986782,0.008257958,0.02849991,0.8680456],"study_design_scores_gemma":[0.009169015,0.01974477,0.01817941,0.006859985,0.001373914,0.008001532,0.003149036,0.4755535,0.001279895,0.00001836934,0.4560191,0.0006514672],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5637552,0.001120953,0.4300115,0.003853966,0.0005249929,0.0002818587,0.00002918523,0.0001164098,0.0003058829],"genre_scores_gemma":[0.9699945,0.0004140361,0.02884145,0.0001410516,0.0005061458,0.00001357888,0.00001116894,0.00001416106,0.00006393767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8673941,"threshold_uncertainty_score":0.2871575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02048698716761408,"score_gpt":0.2815402870200218,"score_spread":0.2610532998524077,"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."}}