{"id":"W4391310816","doi":"10.1016/j.jpurol.2024.01.020","title":"Application of STREAM-URO and APPRAISE-AI reporting standards for artificial intelligence studies in pediatric urology: A case example with pediatric hydronephrosis","year":2024,"lang":"en","type":"review","venue":"Journal of Pediatric Urology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; University Health Network; Princess Margaret Cancer Centre; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Medicine; Pediatric urology; Hydronephrosis; Urology; Medical physics; General surgery; Internal medicine; Urinary system","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02856498,0.0005496146,0.001628175,0.005629667,0.0006517274,0.003663488,0.001988256,0.002129665,0.002075679],"category_scores_gemma":[0.05113335,0.0003381372,0.00170169,0.004202727,0.001317041,0.002031145,0.001979964,0.002509956,0.0008368803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001449598,"about_ca_system_score_gemma":0.009279334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003030525,"about_ca_topic_score_gemma":0.005642155,"domain_scores_codex":[0.9861135,0.00481683,0.005421299,0.00048862,0.00294313,0.0002166134],"domain_scores_gemma":[0.9529194,0.03267128,0.004322017,0.001432677,0.008297784,0.0003568293],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001092061,0.00006738034,0.002609998,0.04710276,0.0003294491,0.0005393893,0.0008686808,0.0004058239,0.001200757,0.01601091,0.0210623,0.9096934],"study_design_scores_gemma":[0.00007840752,0.0002034782,0.009686663,0.1053765,0.001586012,0.003597867,0.0009717564,0.0008602126,0.002927618,0.009591407,0.8649771,0.00014299],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.00242334,0.9602489,0.01254108,0.009308626,0.001382624,0.000955336,0.0007728966,0.0001665694,0.01220059],"genre_scores_gemma":[0.02464484,0.9061723,0.05899197,0.004793232,0.0007024517,0.001024087,0.001513435,0.00006760997,0.002090058],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.971435,"threshold_uncertainty_score":0.1510679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3080888171638675,"score_gpt":0.5137034622551596,"score_spread":0.2056146450912921,"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."}}