{"id":"W4316506181","doi":"10.1016/j.ctro.2023.100582","title":"Empirical planning target volume modeling for high precision MRI guided intracranial radiotherapy","year":2023,"lang":"en","type":"article","venue":"Clinical and Translational Radiation Oncology","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Nuclear medicine; Radiation therapy; Magnetic resonance imaging; Medicine; Radiation treatment planning; Optic chiasm; Population; Margin (machine learning); Image fusion; Radiology; Fusion; Computer science; Anatomy","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":[0.0007771734,0.0004918862,0.0005162959,0.0003978306,0.0002026808,0.0006823143,0.001122723,0.0007371556,0.001182151],"category_scores_gemma":[0.002706444,0.0005542548,0.0007219053,0.0004028117,0.0005065727,0.0003830054,0.0004962213,0.00072569,0.0002350914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001102048,"about_ca_system_score_gemma":0.0007780608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01372803,"about_ca_topic_score_gemma":0.008010034,"domain_scores_codex":[0.9996946,0.0001362694,0.00001098238,0.00005006551,0.00007770525,0.00003041264],"domain_scores_gemma":[0.9990784,0.0006162478,0.0001530209,0.00004796693,0.00007800302,0.00002632553],"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":[0.000005854114,0.000002643323,0.0002493126,0.000004374838,0.00000408135,0.000009825801,0.000007011503,0.9980618,0.0001006353,0.0004167474,0.0000425605,0.001095119],"study_design_scores_gemma":[0.000002371044,0.000005079157,0.0001289342,0.000001627502,0.00000171369,0.000008691092,0.000002119586,0.9991122,0.00005852179,0.0005375126,0.0001395374,0.000001638138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.153478,0.000764607,0.8389319,0.0004037077,0.00003382467,0.0001279361,0.0005422398,0.0005864006,0.005131379],"genre_scores_gemma":[0.9428781,0.0003241361,0.05317502,0.00009287203,0.00002543517,0.0001909547,0.0003674886,0.0001571446,0.00278889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01372803,"threshold_uncertainty_score":0.02729625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06886222046873014,"score_gpt":0.4462195031500535,"score_spread":0.3773572826813233,"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."}}