{"id":"W4244338889","doi":"10.32920/ryerson.14647599.v1","title":"Machine Learning Optimization for Prostate Brachytherapy Treatment Planning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Brachytherapy; Prostate brachytherapy; Radiation treatment planning; Dosimetry; Prostate cancer; Medical physics; Prostate; Medicine; Radiation therapy; Computer science; Plan (archaeology); Nuclear medicine; Radiology; Cancer; Internal medicine","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":[0.00148428,0.0006466644,0.0009381308,0.0006179223,0.0003926417,0.0008360835,0.0005341101,0.0009549937,0.002159735],"category_scores_gemma":[0.003957222,0.000634091,0.0007082634,0.0008698119,0.0007344227,0.0005272233,0.0006829675,0.001243058,0.0004709739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00171387,"about_ca_system_score_gemma":0.001400691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008567395,"about_ca_topic_score_gemma":0.005752363,"domain_scores_codex":[0.9994251,0.0002951542,0.00002212715,0.00006534815,0.000155889,0.00003652975],"domain_scores_gemma":[0.9981714,0.001469817,0.00009183209,0.00005106188,0.0001835218,0.00003229475],"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":[0.00001376823,0.000009111499,0.0001032981,0.00002577723,0.00000990303,0.000007177676,0.00001014647,0.983182,0.0002725713,0.001603464,0.0003813303,0.0143814],"study_design_scores_gemma":[0.00000201829,0.0000047095,0.00004633969,0.000002414285,8.441598e-7,0.000001837562,0.000001457821,0.9982606,0.0001042829,0.001357729,0.0002162845,0.000001461032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01567621,0.001060926,0.9779997,0.0005081599,0.00006047893,0.00007552229,0.00009495129,0.0007140341,0.003810027],"genre_scores_gemma":[0.5307459,0.0009814008,0.4596801,0.0002330449,0.0001229766,0.0004795858,0.0003430027,0.0003941945,0.007019853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008567395,"threshold_uncertainty_score":0.01703507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03851515967791665,"score_gpt":0.3273774585237731,"score_spread":0.2888622988458565,"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."}}