{"id":"W4394291811","doi":"10.6084/m9.figshare.5125285","title":"Supplementary Material for: Early Anticipation of Candidacy for Intra-Arterial Reperfusion Therapy Based on Baseline Clinical Stroke Subtypes: Comparison with Multiparametric MRI Taken within 4.5 Hours from Stroke Onset","year":2013,"lang":"en","type":"dataset","venue":"Figshare","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Candidacy; Anticipation (artificial intelligence); Stroke (engine); Medicine; Baseline (sea); Cardiology; Internal medicine; Physical therapy; Physical medicine and rehabilitation; Computer science; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001006204,0.00094526,0.0008638905,0.001947142,0.0006706768,0.001344983,0.001409051,0.001360889,0.7935453],"category_scores_gemma":[0.01897691,0.0005424486,0.0006900109,0.002028619,0.0001915659,0.001140898,0.0009773392,0.0006882702,0.1743991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006331224,"about_ca_system_score_gemma":0.001134843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004568123,"about_ca_topic_score_gemma":0.007621884,"domain_scores_codex":[0.9995511,0.00009246168,0.0001155532,0.0001049378,0.00008461328,0.00005127612],"domain_scores_gemma":[0.9891729,0.00661943,0.001063159,0.0007898216,0.00175571,0.0005989834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009749549,0.0001959544,0.005894637,0.001506999,0.00007115403,0.0002343084,0.00004664313,0.000281271,0.0003373254,0.0007293702,0.9472573,0.04247002],"study_design_scores_gemma":[0.004650032,0.001035691,0.1305969,0.004155759,0.0003141059,0.004193611,0.0005739619,0.004654489,0.002091835,0.01477304,0.8326958,0.000264719],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003380516,0.0002524742,0.002457886,0.001233102,0.0006604621,0.000509538,0.9773149,0.001404096,0.01278694],"genre_scores_gemma":[0.03437805,0.001057422,0.01833342,0.002455686,0.00104355,0.003343239,0.8819606,0.001338019,0.05609005],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7935453,"threshold_uncertainty_score":0.2944825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06111956545499,"score_gpt":0.3422602100047801,"score_spread":0.28114064454979,"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."}}