{"id":"W2911693327","doi":"10.1161/str.50.suppl_1.wp196","title":"Abstract WP196: MicroRNA and IGF-1 as Predictive Biomarkers for Stroke Outcomes","year":2019,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Medicine; Stroke (engine); Emergency department; Internal medicine; Prospective cohort study; Correlation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0006284332,0.0005503994,0.0004252469,0.0007126983,0.0001490281,0.0007841876,0.0002399974,0.0004004601,0.006876652],"category_scores_gemma":[0.001501756,0.0001089429,0.0003426554,0.0006822629,0.0001738427,0.0002814925,0.0002726584,0.000471849,0.002056958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001843632,"about_ca_system_score_gemma":0.000270539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003624632,"about_ca_topic_score_gemma":0.0003345608,"domain_scores_codex":[0.9998068,0.00005240938,0.00002398996,0.00004039561,0.00006114085,0.00001534093],"domain_scores_gemma":[0.999393,0.0002041092,0.0001831243,0.00002802918,0.0001068112,0.00008483662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01146035,0.0005112438,0.6412634,0.001236184,0.001034871,0.000926144,0.0001528489,0.001031849,0.04835153,0.000468718,0.02272229,0.2708406],"study_design_scores_gemma":[0.0003656383,0.002005932,0.9523805,0.0002837036,0.0009692291,0.002531203,0.0001350554,0.00420015,0.01767946,0.001176023,0.01821924,0.00005381908],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.9187386,0.04130408,0.006100521,0.003948134,0.001570578,0.0002410976,0.01421791,0.0004650628,0.01341408],"genre_scores_gemma":[0.9738674,0.005146142,0.006196209,0.0005868904,0.001236945,0.0001901442,0.004930995,0.00006274747,0.007782579],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.006876652,"threshold_uncertainty_score":0.02300465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0103840735885896,"score_gpt":0.2686643072631353,"score_spread":0.2582802336745457,"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."}}