{"id":"W2023560768","doi":"10.1016/j.ijrobp.2007.04.004","title":"Development of Multiorgan Finite Element-Based Prostate Deformation Model Enabling Registration of Endorectal Coil Magnetic Resonance Imaging for Radiotherapy Planning","year":2007,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network; Mount Sinai Hospital; Princess Margaret Cancer Centre","funders":"","keywords":"Fiducial marker; Medicine; Magnetic resonance imaging; Prostate; Prostate cancer; Radiation treatment planning; Deformation (meteorology); Nuclear medicine; Image-guided radiation therapy; Radiation therapy; Radiology; Medical imaging; Image registration; Artificial intelligence; Cancer; Materials science; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009234418,0.0001695014,0.0003223292,0.0002097765,0.00007685266,0.00001449318,0.0002626712,0.0000698091,0.00001216188],"category_scores_gemma":[0.00005411302,0.0001677227,0.0001351599,0.0001201809,0.0001016728,0.0003568998,0.0000142964,0.0001798553,1.141847e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000393079,"about_ca_system_score_gemma":0.0004803149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007925568,"about_ca_topic_score_gemma":0.000001408293,"domain_scores_codex":[0.9981623,0.00005406292,0.00119008,0.00015644,0.00022892,0.0002082514],"domain_scores_gemma":[0.9966767,0.0003461502,0.002089195,0.00009752542,0.0007442537,0.00004621932],"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":[0.0008093318,0.0003001504,0.1172148,0.00001819077,0.0001380944,0.000001237899,0.002586233,0.04943835,0.1226071,0.006532731,0.00004670347,0.7003071],"study_design_scores_gemma":[0.008330121,0.0006990655,0.003584821,0.0001873224,0.00004763987,0.000007397452,0.0003401356,0.5402346,0.425505,0.007622583,0.01309462,0.0003466398],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1646974,0.0005142324,0.8340366,0.00008759772,0.0002307609,0.0002764809,0.00006422713,0.00001841427,0.00007430922],"genre_scores_gemma":[0.6763545,0.00003308778,0.3230409,0.00007025147,0.0003259942,0.00001624316,0.000133329,0.00001760034,0.000008067831],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6999605,"threshold_uncertainty_score":0.6839535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654930782733799,"score_gpt":0.3286042025857152,"score_spread":0.3120548947583772,"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."}}