{"id":"W4250408289","doi":"10.32920/ryerson.14654556","title":"Non-Invasive Modeling of Intracranial Hypertension from Physiological Channels","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Icahn School of Medicine at Mount Sinai","keywords":"Intracranial pressure; Blood pressure; Medicine; Cluster analysis; Catheter; Computer science; Cerebrospinal fluid; Artificial intelligence; Biomedical engineering; Anesthesia; Internal medicine; Surgery","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":[0.0001940638,0.0004360988,0.0003489436,0.0002383176,0.0002033322,0.0007670318,0.0005147399,0.0007719928,0.0009577018],"category_scores_gemma":[0.000556923,0.0002495935,0.0006892364,0.0002362865,0.0002979405,0.0004068555,0.0004338084,0.0006069049,0.0003127058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002608598,"about_ca_system_score_gemma":0.0005825681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005476641,"about_ca_topic_score_gemma":0.00294949,"domain_scores_codex":[0.9999081,0.00001896272,0.000004610766,0.00002668841,0.00002761712,0.00001394183],"domain_scores_gemma":[0.9998705,0.00006347203,0.00001923429,0.000008707872,0.00002984089,0.000008246523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000101901,0.00007048056,0.002147393,0.0001125134,0.00005300381,0.0002686065,0.00008965252,0.9485158,0.01127986,0.005858428,0.001169461,0.03033296],"study_design_scores_gemma":[0.000001871292,0.00001378174,0.0003348609,0.000003439303,0.000004605083,0.00002032866,0.000003601318,0.9984043,0.0003749822,0.0004033073,0.0004308723,0.000004112786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1453045,0.001917163,0.8378032,0.0007296838,0.000291412,0.0001395253,0.0006039941,0.0008090436,0.01240147],"genre_scores_gemma":[0.9525713,0.002095097,0.03400375,0.00007958953,0.000119109,0.00019179,0.0003898334,0.00005133686,0.01049813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005476641,"threshold_uncertainty_score":0.01088953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024418256981934,"score_gpt":0.2715285843719231,"score_spread":0.1690867586737296,"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."}}