{"id":"W4308977915","doi":"10.1093/neuonc/noac209.200","title":"RADT-10. THE LOST METASTASES: DEEP LEARNING’S POTENTIAL IN RADIOSURGERY QUALITY ASSURANCE","year":2022,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; Centre Hospitalier de l’Université de Montréal; Centre intégré de santé et de services sociaux de la Montérégie-Centre; Centre intégré de santé et de services sociaux de Chaudière-Appalaches; Centre Intégré de Santé et de Services Sociaux des Laurentides; GDI Integrated Facility Services (Canada)","funders":"","keywords":"Medicine; Radiosurgery; False positive paradox; Radiology; Quality assurance; Medical physics; Radiation therapy; Nuclear medicine; Artificial intelligence; Computer science; Pathology","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":[0.002529021,0.0006067291,0.0003234004,0.0004768262,0.0002056696,0.000856764,0.001088547,0.001088462,0.002983379],"category_scores_gemma":[0.003964932,0.0002341336,0.0003535633,0.0003413333,0.0003792739,0.0006468034,0.0008392395,0.0009961891,0.000642342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059219,"about_ca_system_score_gemma":0.00103099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005936138,"about_ca_topic_score_gemma":0.006042135,"domain_scores_codex":[0.9994681,0.0002011996,0.00002747871,0.00009465913,0.0001459949,0.0000625571],"domain_scores_gemma":[0.9986669,0.0006883263,0.00012534,0.0001114599,0.0002983815,0.0001095965],"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":[0.0006261826,0.0003794267,0.01697241,0.0003506754,0.0001606311,0.000254194,0.00007052191,0.430847,0.007852161,0.007048497,0.02632935,0.509109],"study_design_scores_gemma":[0.00001995285,0.000126466,0.001137636,0.00002841516,0.00001282653,0.00006044075,0.00001208469,0.9880983,0.006216934,0.001870122,0.002406062,0.00001076195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4762188,0.009589912,0.4599246,0.008976243,0.0008219463,0.0003403434,0.002915828,0.01586107,0.02535119],"genre_scores_gemma":[0.8674431,0.0008302596,0.1220813,0.0005416389,0.00007417142,0.0001238461,0.00202427,0.0002676733,0.006613763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005936138,"threshold_uncertainty_score":0.01337492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740029781607642,"score_gpt":0.3234356014842888,"score_spread":0.3060353036682124,"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."}}