{"id":"W4239538787","doi":"10.2196/preprints.33357","title":"A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study (Preprint)","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Kidney Stones and Urolithiasis Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University of Toronto; York University","funders":"","keywords":"Shock wave lithotripsy; Percutaneous nephrolithotomy; Machine learning; Medicine; Artificial intelligence; Decision tree; Algorithm; Data set; Computer science; Data mining; Lithotripsy; Surgery; Percutaneous","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":[0.00563658,0.001149866,0.000919121,0.001235757,0.0005122222,0.0009718918,0.001167895,0.001207547,0.001464581],"category_scores_gemma":[0.006500127,0.000358378,0.001512902,0.0006462584,0.0003907856,0.0005310235,0.0007183634,0.001510195,0.0003728266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001695033,"about_ca_system_score_gemma":0.001767258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03752334,"about_ca_topic_score_gemma":0.01656697,"domain_scores_codex":[0.9991112,0.0004747683,0.00007126854,0.0001455099,0.00009774709,0.0000994144],"domain_scores_gemma":[0.9939557,0.004285168,0.0002864483,0.0001949358,0.001149676,0.0001280786],"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":[0.000453541,0.0008989769,0.0333281,0.0001258786,0.0002929356,0.00009335483,0.00006877596,0.9276043,0.0005273258,0.0003030737,0.001273287,0.03503041],"study_design_scores_gemma":[0.00001295677,0.0001121441,0.002221417,0.0000102626,0.00001971054,0.00000750045,0.0000176807,0.9971803,0.000257151,0.0000756938,0.00007983933,0.000005299032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9465733,0.0008291108,0.04801979,0.0006902351,0.0001206434,0.0003434684,0.0009529224,0.0005143854,0.001956212],"genre_scores_gemma":[0.9793298,0.0002143371,0.01755772,0.0000956888,0.00002444719,0.0002769499,0.001454678,0.00001568923,0.001030801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03752334,"threshold_uncertainty_score":0.07460982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1174582186084276,"score_gpt":0.3356566069808523,"score_spread":0.2181983883724247,"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."}}