{"id":"W3134308110","doi":"10.3389/fped.2021.618236","title":"Consensus Approach for Standardizing the Screening and Classification of Preterm Brain Injury Diagnosed With Cranial Ultrasound: A Canadian Perspective","year":2021,"lang":"en","type":"article","venue":"Frontiers in Pediatrics","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Mount Sinai Hospital; Université Laval; Dalhousie University; University of Calgary","funders":"Canadian Institutes of Health Research; Hospital for Sick Children; Ontario Ministry of Health and Long-Term Care","keywords":"Medicine; Intraventricular hemorrhage; Germinal matrix; Neuroimaging; White matter; Intensive care; Echoencephalography; Periventricular leukomalacia; Traumatic brain injury; Intracerebral hemorrhage; Intensive care medicine; Radiology; Magnetic resonance imaging; Gestational age; Surgery; Glasgow Coma Scale; Pregnancy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000313634,0.0001063199,0.00025254,0.0001672127,0.0001035785,0.00001428429,0.00005496949,0.0001019082,0.000001628023],"category_scores_gemma":[0.001022752,0.00008082232,0.00004471667,0.0003351173,0.0002020283,0.00002991594,0.00001461319,0.0001969048,5.945263e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001328051,"about_ca_system_score_gemma":0.0004853838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001218565,"about_ca_topic_score_gemma":0.001989164,"domain_scores_codex":[0.9991192,0.00006792333,0.0001805354,0.0002685626,0.000145676,0.0002180988],"domain_scores_gemma":[0.9990826,0.0003619975,0.00008220057,0.0001616826,0.0002081304,0.0001034128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002971281,0.0001604643,0.9374784,0.0004885831,0.0001760333,0.0003132842,0.01538408,0.00004491256,0.002279624,0.002190384,0.0171623,0.0213507],"study_design_scores_gemma":[0.01508926,0.002017618,0.8169513,0.0001478697,0.0009752057,0.001106796,0.1440424,0.006941215,0.0008226364,0.003721826,0.007270811,0.0009129856],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8478096,0.005610317,0.13744,0.003664233,0.0003669006,0.001666807,0.0007352505,0.00002773778,0.002679162],"genre_scores_gemma":[0.9259254,0.0001146124,0.07329421,0.0003104961,0.0001515747,0.00004345107,0.00006437178,0.00001752293,0.0000783564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1286584,"threshold_uncertainty_score":0.3295839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01434090494953497,"score_gpt":0.2556243080483497,"score_spread":0.2412834030988148,"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."}}