{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005886407,0.0008863051,0.0005893435,0.001114733,0.0002211438,0.00121126,0.0006564316,0.0006240062,0.0006667678],"category_scores_gemma":[0.01539437,0.0002493183,0.001117225,0.0005814903,0.0004806492,0.0007582324,0.0009496938,0.0006907501,0.0004251753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007649015,"about_ca_system_score_gemma":0.0005055621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004782234,"about_ca_topic_score_gemma":0.0054038,"domain_scores_codex":[0.997458,0.001167965,0.0001910763,0.0007117987,0.0003394931,0.000131691],"domain_scores_gemma":[0.9905182,0.004979882,0.001886933,0.001174242,0.001190966,0.0002498535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002407573,0.0002778994,0.7263079,0.0001933538,0.000904112,0.0001128613,0.0004037855,0.1647665,0.006686599,0.0003743925,0.001474274,0.09609065],"study_design_scores_gemma":[0.00006845886,0.0007394026,0.1877626,0.00003710784,0.0002645247,0.0002761725,0.0001602051,0.8024817,0.006257277,0.001182899,0.0006935689,0.00007613874],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9401454,0.0003637109,0.05679755,0.0001249845,0.00003712342,0.0001012124,0.00105991,0.0004089338,0.0009612922],"genre_scores_gemma":[0.9911352,0.00004537656,0.007271291,0.00002411568,0.0000156016,0.00003763073,0.001196129,0.00003078609,0.0002438367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005886407,"threshold_uncertainty_score":0.03113067,"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."}}