{"id":"W4319007839","doi":"10.1161/str.54.suppl_1.hup4","title":"Abstract HUP4: Svin Mt2020 + Global Mechanical Thrombectomy Access Barrier Score","year":2023,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre","funders":"","keywords":"Medicine; Ranking (information retrieval); Scale (ratio); Weighting; Delphi method; Artificial intelligence; Computer science","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":[0.01013774,0.001076347,0.003181252,0.009358658,0.000612018,0.002096463,0.001248915,0.001096452,0.02151942],"category_scores_gemma":[0.03595295,0.0003731736,0.00586778,0.007318412,0.0005407141,0.00163886,0.002914751,0.0007347234,0.001249969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002650069,"about_ca_system_score_gemma":0.00724792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003693446,"about_ca_topic_score_gemma":0.006982663,"domain_scores_codex":[0.9921209,0.003258421,0.002224962,0.0003412137,0.001705854,0.0003486571],"domain_scores_gemma":[0.9848914,0.008456785,0.002760673,0.0002802554,0.003098484,0.0005124881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"observational","study_design_scores_codex":[0.004474799,0.0001060299,0.03268612,0.625881,0.0161462,0.0003758567,0.0007195615,0.001786995,0.001005996,0.002959226,0.08312211,0.2307361],"study_design_scores_gemma":[0.005897055,0.002557351,0.2743278,0.3690074,0.08132906,0.001891083,0.002271853,0.002974106,0.00227101,0.007684128,0.2493944,0.0003946729],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1293126,0.3289915,0.0151344,0.02123646,0.001876054,0.05503531,0.3956907,0.001205502,0.0515174],"genre_scores_gemma":[0.5398486,0.1763733,0.06272451,0.00536506,0.0008396741,0.09008935,0.1104733,0.00027276,0.01401341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02151942,"threshold_uncertainty_score":0.07198966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03838963697149236,"score_gpt":0.3315823878353441,"score_spread":0.2931927508638517,"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."}}