{"id":"W2807852901","doi":"10.1016/j.neuroimage.2018.06.004","title":"White matter injury in term neonates with congenital heart diseases: Topology & comparison with preterm newborns","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Douglas Mental Health University Institute; Hospital for Sick Children; University of British Columbia; SickKids Foundation; BC Children's Hospital; University of Toronto","funders":"Canadian Institutes of Health Research; March of Dimes Foundation; National Institutes of Health; Olga Mayenfisch Stiftung; Larry L. Hillblom Foundation; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; American Heart Association","keywords":"White matter; Medicine; Term (time); Pediatrics; Cardiology; Topology (electrical circuits); Internal medicine; Physics; Mathematics; Magnetic resonance imaging; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004077331,0.0003189695,0.0005376933,0.0001866017,0.0001097374,0.00004493784,0.0001102855,0.00005902617,0.000774392],"category_scores_gemma":[0.00003083187,0.0002389917,0.00006942021,0.000282984,0.0008324726,0.0001793048,0.0001283438,0.0002780663,0.0005007156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004816889,"about_ca_system_score_gemma":0.0001164305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007334445,"about_ca_topic_score_gemma":0.00107238,"domain_scores_codex":[0.9982353,0.00008082548,0.0002984518,0.0005752242,0.0002876467,0.0005225114],"domain_scores_gemma":[0.9988922,0.00006485723,0.00005357891,0.0005671321,0.0001525261,0.0002696977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002777697,0.0002658555,0.9890652,0.00007556686,0.00006968687,0.0002152209,0.0006254355,1.678692e-7,0.0007537993,0.000002483988,0.005359206,0.0007896381],"study_design_scores_gemma":[0.001903902,0.001730264,0.9894827,0.00008080601,0.000200274,0.0001956244,0.000131924,0.00004026104,0.0002092832,0.00001152526,0.005748181,0.0002652764],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908068,0.0001612109,0.00001963472,0.001831189,0.0001221528,0.0007703087,0.00009697878,0.0001187687,0.006073016],"genre_scores_gemma":[0.9950144,0.00000429285,0.0005638209,0.002403914,0.0002112498,0.00006871877,0.0000326635,0.00006459239,0.001636325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004436691,"threshold_uncertainty_score":0.9745798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01598640747798366,"score_gpt":0.2971388933793503,"score_spread":0.2811524859013667,"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."}}