{"id":"W4417458446","doi":"10.48550/arxiv.2512.13434","title":"Self-Supervised Ultrasound Representation Learning for Renal Anomaly Prediction in Prenatal Imaging","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Pediatric Urology and Nephrology Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Ultrasound; Prenatal diagnosis; Multicystic dysplastic kidney; Binary classification; Urinary system; Representation (politics); Medical imaging","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001437481,0.001075267,0.0007733259,0.0006392021,0.0002216078,0.0005681214,0.001341603,0.001041692,0.0009438329],"category_scores_gemma":[0.003035243,0.0003456441,0.0009304367,0.0004269227,0.0003954849,0.0008158809,0.0007344598,0.001266406,0.000520485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007412392,"about_ca_system_score_gemma":0.001130189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008669234,"about_ca_topic_score_gemma":0.01044276,"domain_scores_codex":[0.9995178,0.0001401228,0.0000262209,0.0001594306,0.00007967649,0.00007673879],"domain_scores_gemma":[0.9988486,0.000564009,0.0001033409,0.0001697231,0.0002570533,0.00005730589],"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":[0.0005210429,0.0004110624,0.01399347,0.0001598765,0.0002445992,0.0002416394,0.0001077902,0.7132317,0.0144946,0.001334688,0.006905064,0.2483545],"study_design_scores_gemma":[0.000007400974,0.00004790759,0.000704037,0.000007028739,0.00001207666,0.00003417945,0.000007653031,0.9960284,0.002400009,0.0005198263,0.00022553,0.000006080489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5894624,0.00188592,0.393418,0.0009553644,0.000149293,0.0001395621,0.001539155,0.00949268,0.002957467],"genre_scores_gemma":[0.9438713,0.0002555661,0.05013904,0.0002672854,0.00005030783,0.00008582037,0.002922951,0.0001401001,0.002267525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008669234,"threshold_uncertainty_score":0.01723754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256834105261566,"score_gpt":0.2926777899632222,"score_spread":0.2669943794370656,"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."}}