{"id":"W4220817146","doi":"10.2196/33357","title":"A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study","year":2022,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Kidney Stones and Urolithiasis Treatments","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University of Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Percutaneous nephrolithotomy; Machine learning; Medicine; Shock wave lithotripsy; Artificial intelligence; Decision tree; AdaBoost; Data set; Algorithm; Computer science; Support vector machine; Surgery; Lithotripsy; Percutaneous","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.006319941,0.001326564,0.001043503,0.001521029,0.0005928859,0.0009573363,0.001360546,0.001319955,0.001538736],"category_scores_gemma":[0.006766894,0.0004354047,0.001572684,0.0007493446,0.000431699,0.0006274158,0.0007773786,0.001689635,0.0002926119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001866578,"about_ca_system_score_gemma":0.002126108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03395835,"about_ca_topic_score_gemma":0.01507367,"domain_scores_codex":[0.9989809,0.0005302069,0.0000784082,0.0001573603,0.0001356411,0.0001174836],"domain_scores_gemma":[0.9930995,0.005030654,0.0003557988,0.0001905553,0.001207996,0.0001155408],"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":[0.0002449617,0.0005472868,0.01883377,0.00008944698,0.0002034541,0.00007762896,0.00004409119,0.9584236,0.0003108993,0.0002694929,0.0005626822,0.02039281],"study_design_scores_gemma":[0.000009559996,0.00006884773,0.001016962,0.000007017032,0.00001260203,0.000006437029,0.000008784214,0.998624,0.0001257584,0.0000670892,0.00004962274,0.000003340117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9144964,0.00101234,0.07955398,0.0006779947,0.00009139573,0.0004230338,0.0008062858,0.0005359498,0.002402617],"genre_scores_gemma":[0.9794272,0.0002303737,0.01839405,0.00007249873,0.00002005226,0.0002830898,0.0007958191,0.00001218455,0.0007646339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03395835,"threshold_uncertainty_score":0.06752139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2240854306398653,"score_gpt":0.4657708169363353,"score_spread":0.24168538629647,"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."}}