{"id":"W2941293189","doi":"10.1097/01.ju.0000555962.29512.0b","title":"PD26-02 FLUOROSCOPIC TARGETING OF RENAL CALCULI DURING EXTRACORPOREAL SHOCKWAVE LITHOTRIPSY USING A MACHINE LEARNING ALGORITHM","year":2019,"lang":"en","type":"article","venue":"The Journal of Urology","topic":"Kidney Stones and Urolithiasis Treatments","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Extracorporeal shockwave lithotripsy; Lithotripsy; Algorithm; Urology; Surgery; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008597092,0.0004648137,0.000348585,0.0008904723,0.0002401698,0.00106389,0.0006489101,0.0009757418,0.05564547],"category_scores_gemma":[0.00276632,0.0002820413,0.0005054679,0.0005258442,0.0001468295,0.0005057016,0.0005800886,0.0006525882,0.01513486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003242361,"about_ca_system_score_gemma":0.000617768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001820077,"about_ca_topic_score_gemma":0.002404355,"domain_scores_codex":[0.999706,0.00005458911,0.00002031115,0.00005084486,0.000146192,0.00002204727],"domain_scores_gemma":[0.9992337,0.0002772524,0.00004037141,0.00007049472,0.0003023851,0.00007570124],"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":[0.001307727,0.0003134464,0.004010446,0.000300918,0.00009490085,0.0006235749,0.00005094634,0.0284808,0.02259143,0.002017336,0.2369101,0.7032984],"study_design_scores_gemma":[0.0006423402,0.001108384,0.01512671,0.0002215057,0.00006973498,0.002731322,0.00006286592,0.6136796,0.05276369,0.00263348,0.3108313,0.0001291485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08349755,0.003645318,0.7333254,0.003351707,0.002148056,0.00117951,0.006939237,0.04435491,0.1215584],"genre_scores_gemma":[0.2795235,0.002530274,0.5089571,0.0007313135,0.000809881,0.0006844435,0.01401484,0.002993291,0.1897553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05564547,"threshold_uncertainty_score":0.1861526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01312824894550428,"score_gpt":0.2658245469328584,"score_spread":0.2526962979873541,"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."}}