{"id":"W3029267245","doi":"","title":"The Alberta Stroke Program Early CT Score (ASPECTS) for Early Outcome Prediction in Post-Cardiac Arrest Patients","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Medicine; Cardiology; Stroke (engine); Internal medicine; Outcome (game theory); 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":[],"consensus_categories":[],"category_scores_codex":[0.0009608663,0.0003508248,0.0003580714,0.0007617351,0.0002951357,0.001223015,0.0003812054,0.0003538779,0.002041113],"category_scores_gemma":[0.004849295,0.0001647368,0.0004386274,0.0006909605,0.0001990409,0.0003164275,0.0004993656,0.00130949,0.0005156187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009617786,"about_ca_system_score_gemma":0.003063736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05197141,"about_ca_topic_score_gemma":0.1050168,"domain_scores_codex":[0.9997407,0.00004866151,0.00001382912,0.00002358531,0.000125531,0.00004774958],"domain_scores_gemma":[0.9993907,0.0001117193,0.00007923083,0.00002035298,0.0002666425,0.0001313094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001573279,0.0005816683,0.7928246,0.0001050857,0.0004718528,0.0000779939,0.0002726178,0.001172687,0.0004284591,0.0008018885,0.02346987,0.17822],"study_design_scores_gemma":[0.00008168805,0.000225458,0.9937299,0.0001400096,0.000254147,0.0000770294,0.0002091685,0.001208009,0.0002314147,0.0007427263,0.003077493,0.00002295695],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9471865,0.00931008,0.0008377716,0.004781124,0.0006061426,0.0002047625,0.00440417,0.00008111308,0.03258837],"genre_scores_gemma":[0.9833882,0.0046598,0.002185903,0.0004062544,0.0002845451,0.0001023973,0.003065011,0.00002296123,0.005885042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05197141,"threshold_uncertainty_score":0.1033378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297590252874486,"score_gpt":0.299892421888323,"score_spread":0.2869165193595781,"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."}}