{"id":"W4404621330","doi":"10.2147/jmdh.s495952","title":"Utility of the ASPECT Score for Predicting Intracranial Hemorrhage Following Intravenous Thrombolysis in Patients with Suspected MCA Infarction: Insights from the Northern Thai Stroke Registry","year":2024,"lang":"en","type":"article","venue":"Journal of Multidisciplinary Healthcare","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Faculty of Medicine, Chiang Mai University; Chiang Mai University","keywords":"Thrombolysis; Medicine; Stroke (engine); Infarction; Ischemic stroke; Cerebral infarction; Emergency medicine; Internal medicine; Intensive care medicine; Myocardial infarction; Ischemia","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004478349,0.0002686835,0.0006440608,0.0001507549,0.0002590081,0.00003385233,0.0003852683,0.0001479138,0.00001575324],"category_scores_gemma":[0.0002707969,0.0001426549,0.0003933577,0.0005386068,0.0001255966,0.0002030472,0.0002010348,0.001070043,6.072216e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003685744,"about_ca_system_score_gemma":0.0005925489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004462345,"about_ca_topic_score_gemma":0.001600549,"domain_scores_codex":[0.997217,0.0001647766,0.001050437,0.0003538633,0.0008912541,0.0003226862],"domain_scores_gemma":[0.9978469,0.0004346874,0.0006373083,0.0005752625,0.0003762096,0.0001296043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0017578,0.0003253661,0.9832624,0.0005322939,0.0005919384,0.0002431249,0.005815622,0.0000497331,0.0003658798,0.000005426897,0.0001309408,0.006919459],"study_design_scores_gemma":[0.003242263,0.0009978166,0.9827623,0.002116381,0.0005380904,0.00006689494,0.006016582,0.003659117,0.0003374798,0.00008683334,0.00004743894,0.0001288355],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988562,0.00149885,0.0003503932,0.006980042,0.000979604,0.001333245,0.0001554398,0.00002991322,0.0001105132],"genre_scores_gemma":[0.9975538,0.00002612807,0.001527173,0.00008048757,0.000677293,0.00001514316,0.00004039,0.00004497439,0.00003467428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008991738,"threshold_uncertainty_score":0.5817298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01434038853452045,"score_gpt":0.2699710636120061,"score_spread":0.2556306750774856,"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."}}