{"id":"W3213811889","doi":"10.21203/rs.3.rs-1078827/v1","title":"Predicting Survival in Patients with Glioblastoma Using MRI Radiomic Features Extracted from Radiation Planning Volumes","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"Terry Fox Foundation","keywords":"Glioblastoma; Radiation therapy; Medicine; Radiation treatment planning; Radiology; Nuclear medicine; Computer science; Medical physics; Artificial intelligence; Cancer research","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":[0.0009246485,0.0004629278,0.0003318391,0.0008163977,0.0001106728,0.0004539127,0.0002164134,0.0002215858,0.0005111704],"category_scores_gemma":[0.00267191,0.0001112263,0.0005145553,0.0002736103,0.0001418185,0.0002211039,0.0003486962,0.0003022585,0.0001886393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000264381,"about_ca_system_score_gemma":0.0002473739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001844181,"about_ca_topic_score_gemma":0.001510782,"domain_scores_codex":[0.9998161,0.00006667709,0.00001782587,0.00004021102,0.0000317942,0.00002728813],"domain_scores_gemma":[0.9989811,0.0004449193,0.0002665809,0.00007769423,0.0001382861,0.00009145369],"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":[0.0008246537,0.0001063969,0.9642385,0.00002320988,0.000163227,0.00008352231,0.00003433553,0.0160933,0.001345558,0.0000427888,0.0003193973,0.0167251],"study_design_scores_gemma":[0.00004658923,0.001109232,0.7968795,0.00003124216,0.0002728644,0.0004564523,0.0001332764,0.197497,0.00259995,0.0004568617,0.0004861816,0.00003087411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977762,0.0001107599,0.001643696,0.00003760968,0.000005589726,0.000006234738,0.0002532815,0.00002349411,0.0001432206],"genre_scores_gemma":[0.9991506,0.00002311425,0.0003419184,0.000005340317,0.00000398754,0.000004443212,0.0004323806,0.000002042507,0.00003621497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001844181,"threshold_uncertainty_score":0.004890084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02313076229484172,"score_gpt":0.3602724441656402,"score_spread":0.3371416818707985,"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."}}