{"id":"W4402141023","doi":"10.3389/fneur.2024.1413795","title":"Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study","year":2024,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Logistic regression; Radiomics; Receiver operating characteristic; Stroke (engine); Retrospective cohort study; Radiology; Cohort; Internal medicine","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":[0.01054622,0.001376324,0.0009354923,0.001264628,0.0004491739,0.00119913,0.0009117694,0.0008027955,0.0006941057],"category_scores_gemma":[0.01979421,0.0005206522,0.001539801,0.0004514507,0.0006024658,0.001041541,0.0007796554,0.0009883124,0.0002313216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035692,"about_ca_system_score_gemma":0.0008319701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004973266,"about_ca_topic_score_gemma":0.003669907,"domain_scores_codex":[0.997799,0.001392608,0.0001084398,0.000397678,0.0001681445,0.000134013],"domain_scores_gemma":[0.9868663,0.008967772,0.0009291106,0.001506208,0.001109509,0.0006210555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00517013,0.002897005,0.9399515,0.00004882467,0.0009244884,0.0002708156,0.0003397563,0.02864693,0.0008096472,0.0001961614,0.0004522467,0.02029251],"study_design_scores_gemma":[0.0006344289,0.005855842,0.2936864,0.0000302201,0.0005138041,0.0004181942,0.0005365501,0.6956503,0.001496617,0.0006948269,0.000401703,0.00008105904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961609,0.00007513838,0.003459579,0.00004483578,0.000008572332,0.00005860154,0.00009322041,0.00001812376,0.00008114755],"genre_scores_gemma":[0.9973804,0.00003885964,0.002051117,0.00002011809,0.0000152464,0.00004788569,0.000365003,0.000006655916,0.00007461546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01054622,"threshold_uncertainty_score":0.05577445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117144819884983,"score_gpt":0.2577806589492412,"score_spread":0.2460661769607429,"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."}}