{"id":"W4406260838","doi":"10.1101/2025.01.08.25320190","title":"Neuroprognostication via Spatially-Informed Machine Learning Following Hypoxic-Ischemic Injury","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; SickKids Foundation","funders":"Alliance de recherche numérique du Canada; TD Bank","keywords":"Ischemic injury; Hypoxia (environmental); Computer science; Medicine; Psychology; Artificial intelligence; Cardiology; Ischemia; Chemistry; Oxygen","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002404675,0.0004546562,0.0003977045,0.0008015178,0.0002538075,0.0009117645,0.0005669265,0.0003819091,0.001343823],"category_scores_gemma":[0.01009643,0.000170112,0.000637423,0.0004247016,0.0004117444,0.0006824775,0.0006772223,0.000949525,0.0002822477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007348438,"about_ca_system_score_gemma":0.0007072873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004996634,"about_ca_topic_score_gemma":0.006968097,"domain_scores_codex":[0.9994179,0.0002895777,0.00003629281,0.0001437403,0.00006411871,0.00004839445],"domain_scores_gemma":[0.9975191,0.0009960181,0.0008630049,0.0002522037,0.0002648472,0.0001049236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005523291,0.0002179446,0.7719175,0.0001268462,0.0004184145,0.0006134833,0.0002681498,0.1159869,0.002544494,0.002050789,0.002710919,0.1025923],"study_design_scores_gemma":[0.0000318696,0.0004735774,0.2276291,0.0002337919,0.0001455845,0.0007895242,0.0004087266,0.7484107,0.004514916,0.01549702,0.001793797,0.00007146206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9091505,0.0008030859,0.08349206,0.001506285,0.00007537811,0.0001034837,0.002221151,0.0002663397,0.00238161],"genre_scores_gemma":[0.9878064,0.0001735558,0.01086841,0.00009516023,0.00002310476,0.00004989353,0.0005761823,0.00001558563,0.0003917824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004996634,"threshold_uncertainty_score":0.01271731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01512489631156761,"score_gpt":0.2807202202756979,"score_spread":0.2655953239641303,"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."}}