{"id":"W4318920698","doi":"10.1016/j.ejmp.2023.102533","title":"3D dose prediction for Gamma Knife radiosurgery using deep learning and data modification","year":2023,"lang":"en","type":"article","venue":"Physica Medica","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto","funders":"","keywords":"Radiosurgery; Data-driven; Deep learning; Mathematics; Generative adversarial network; Artificial intelligence; Computer science; Statistics; Medicine; Radiation therapy; Radiology","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":[0.0002781873,0.0001226128,0.0001764834,0.00007197901,0.0001952395,0.00002694607,0.000159257,0.00003243991,0.00003352917],"category_scores_gemma":[0.00003483157,0.000121815,0.00003657649,0.0002073777,0.00007422444,0.000300845,0.00007137317,0.0001364918,0.000002867406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002268612,"about_ca_system_score_gemma":0.00003279818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002000169,"about_ca_topic_score_gemma":1.48462e-7,"domain_scores_codex":[0.9990355,0.00003923381,0.0001787575,0.0003594019,0.0001608033,0.0002262853],"domain_scores_gemma":[0.9992364,0.0001931747,0.0001245233,0.0003369904,0.00003377879,0.00007511716],"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":[0.00005293832,0.0001184821,0.008207468,0.00007520088,0.0001705288,0.00000125212,0.0007854651,0.0008597593,0.0619792,0.00168252,0.005258646,0.9208086],"study_design_scores_gemma":[0.0005242837,0.00004820346,0.001450275,0.00005458024,0.00005821596,0.000001160882,0.0001558696,0.9048507,0.00175066,0.002376955,0.08851884,0.0002102597],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.059647,0.0001697622,0.9383725,0.0002786203,0.0001628669,0.0004702756,0.000117254,0.0004551789,0.0003265211],"genre_scores_gemma":[0.9685792,0.0001765883,0.02807977,0.00002384202,0.001564603,0.0001403813,0.001195026,0.00005977439,0.0001808465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9205983,"threshold_uncertainty_score":0.4967473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04975938232010706,"score_gpt":0.3529671789408564,"score_spread":0.3032077966207494,"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."}}