{"id":"W2150911893","doi":"10.1016/j.ijrobp.2008.06.1099","title":"Multiparametric MRI Response during Radiotherapy for Prostate Cancer","year":2008,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Medicine; Prostate cancer; Radiation therapy; Multiparametric MRI; Prostate; Cancer; Radiology; Medical physics; Oncology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002706827,0.0003127047,0.000705453,0.0004220842,0.0002157644,0.0004316529,0.0003168644,0.0007095785,0.002756885],"category_scores_gemma":[0.001960398,0.0001930769,0.0003615053,0.0003073853,0.0002160631,0.0003153287,0.0003089419,0.0008495208,0.0006146696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002393103,"about_ca_system_score_gemma":0.0002015244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001694924,"about_ca_topic_score_gemma":0.001556127,"domain_scores_codex":[0.9998252,0.00004614253,0.000009218724,0.00003104431,0.00003527981,0.00005324833],"domain_scores_gemma":[0.9995426,0.0002127454,0.00008035517,0.00004171778,0.00004727438,0.00007535831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.03187385,0.001309138,0.03230303,0.0004490697,0.000369194,0.0009394626,0.0007515558,0.005899625,0.856658,0.0001083227,0.001542558,0.06779607],"study_design_scores_gemma":[0.0003120949,0.01406271,0.6330147,0.00006554017,0.0007010078,0.003363961,0.0005874559,0.01326227,0.3301756,0.0003667241,0.003943954,0.000143924],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942964,0.001670372,0.001617899,0.0002118939,0.00007162904,0.00003485338,0.0003489064,0.0001416248,0.001606508],"genre_scores_gemma":[0.9986591,0.000161446,0.0001437158,0.00007827373,0.00003148791,0.00001496752,0.0001614042,0.00003674716,0.0007128815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002756885,"threshold_uncertainty_score":0.009222746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0187037154345812,"score_gpt":0.3642482206572484,"score_spread":0.3455445052226672,"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."}}