{"id":"W4389084939","doi":"10.1038/s41598-023-47702-8","title":"Predicting stereotactic radiosurgery outcomes with multi-observer qualitative appearance labelling versus MRI radiomics","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Western University; London Health Sciences Foundation; Government of Ontario","keywords":"Radiomics; Radiogenomics; Medicine; Radiosurgery; Magnetic resonance imaging; Risk stratification; Radiology; Medical physics; Internal medicine; Radiation therapy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004511735,0.0002702279,0.0005658246,0.0003830142,0.0004835239,0.00023664,0.0001292459,0.00008585765,0.00003110803],"category_scores_gemma":[0.001902806,0.000206639,0.0001761428,0.001286227,0.0004505548,0.0002661586,0.00007479861,0.0005478325,0.0000595792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000122805,"about_ca_system_score_gemma":0.00027481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007519076,"about_ca_topic_score_gemma":0.0000126597,"domain_scores_codex":[0.9965249,0.000148637,0.0007187471,0.0009696832,0.0009805808,0.0006574208],"domain_scores_gemma":[0.9977053,0.0004337592,0.0004538951,0.0008553505,0.0002127107,0.0003389717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004489059,0.0002811202,0.9504922,0.000523968,0.0007573133,0.008763804,0.01755745,0.004252161,0.004799725,0.00007623247,0.006974669,0.005072464],"study_design_scores_gemma":[0.02310796,0.0009765734,0.3109093,0.005028178,0.001597502,0.005336617,0.02322068,0.5536125,0.005510151,0.00122551,0.06624164,0.00323348],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862323,0.0002647965,0.002194942,0.001594601,0.008459341,0.00045979,0.000002295039,0.0004496735,0.0003422895],"genre_scores_gemma":[0.9871677,0.00003152261,0.006265779,0.0001377633,0.0001639962,0.00003137214,0.00008715515,0.00007266486,0.006042054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6395829,"threshold_uncertainty_score":0.8426493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04992698681118757,"score_gpt":0.3548771689447402,"score_spread":0.3049501821335526,"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."}}