{"id":"W4417348296","doi":"10.1007/s10489-025-06986-1","title":"Self-supervised learning radiomics nomogram integrating anatomical structures can identify cerebellar hypoplasia in prenatal ultrasound","year":2025,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China; Health Commission of Hubei Province","keywords":"Cerebellar hypoplasia (non-human); Nomogram; Radiomics; Ultrasound; Prenatal diagnosis; Logistic regression; Univariate; Hypoplasia; Magnetic resonance imaging","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":[0.0008465693,0.0007259757,0.0005991005,0.001393542,0.0002286032,0.0007485096,0.0006308495,0.000658548,0.001035307],"category_scores_gemma":[0.003003934,0.0001456245,0.0006159387,0.0004789816,0.0002207242,0.0004850631,0.0004737479,0.0004872323,0.0007992191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002031391,"about_ca_system_score_gemma":0.000555246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003090782,"about_ca_topic_score_gemma":0.004669945,"domain_scores_codex":[0.9994968,0.000138963,0.0000369898,0.0001811801,0.00008878962,0.00005736824],"domain_scores_gemma":[0.9989863,0.0004872285,0.000100877,0.00006447455,0.0003129646,0.00004809465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005669617,0.0002720999,0.06307416,0.000154353,0.0002289463,0.000476047,0.0001485482,0.07446585,0.01724973,0.001071401,0.008787125,0.833505],"study_design_scores_gemma":[0.00003305665,0.000248719,0.03265146,0.00004994029,0.0001975543,0.0008222114,0.0001416258,0.949106,0.01145665,0.001602036,0.00364446,0.00004622662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.334647,0.002516892,0.6470891,0.0005829057,0.0003068269,0.0001755131,0.001410859,0.005483566,0.007787404],"genre_scores_gemma":[0.9173548,0.0005315549,0.07649543,0.000210486,0.0002067586,0.00009714723,0.001834625,0.0002341768,0.003034989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003090782,"threshold_uncertainty_score":0.006145537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009443714665576282,"score_gpt":0.2645301215990227,"score_spread":0.2550864069334464,"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."}}