{"id":"W4402423300","doi":"10.24908/iqurcp17937","title":"Optimizing Prehospital Stroke Diagnosis: Integrating Machine Learning with the FAST Scoring System","year":2024,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Stroke (engine); Scoring system; Machine learning; Medical emergency; Artificial intelligence; Medicine; Engineering; Surgery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002315901,0.0009520116,0.001026995,0.001469213,0.0003226813,0.0009180252,0.0006878158,0.0006735566,0.0009193514],"category_scores_gemma":[0.005936408,0.0002474067,0.0004557484,0.0009185401,0.0001677626,0.001014837,0.0006502997,0.0006047667,0.0005914194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000602004,"about_ca_system_score_gemma":0.001347663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008791325,"about_ca_topic_score_gemma":0.007785444,"domain_scores_codex":[0.9990909,0.0003184219,0.0001003001,0.000173087,0.0001916476,0.0001256486],"domain_scores_gemma":[0.9981167,0.0007107969,0.0002103466,0.0001214443,0.0007643376,0.00007638198],"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":[0.0006427345,0.000518962,0.049024,0.0001339391,0.0001562196,0.0002645646,0.00007764082,0.4246369,0.004696914,0.0005797609,0.007085223,0.5121831],"study_design_scores_gemma":[0.00002513823,0.0002008825,0.006873575,0.00001779392,0.00002518186,0.00004916714,0.00004579884,0.9896317,0.001836162,0.0006378953,0.0006359076,0.00002075346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5923342,0.001427876,0.3951508,0.001044507,0.0002265546,0.0005402081,0.001201919,0.004412695,0.003661204],"genre_scores_gemma":[0.875831,0.000280221,0.1207343,0.0002028963,0.00007886677,0.0002011128,0.001338548,0.00005963734,0.001273407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008791325,"threshold_uncertainty_score":0.01748031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05254938218661034,"score_gpt":0.3311969302797853,"score_spread":0.2786475480931749,"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."}}