{"id":"W2998042392","doi":"","title":"A Comparative Study Between Apparent Diffusion Imaging and Correlated Diffusion Imaging for Prostate Cancer","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prostate cancer; Medicine; Magnetic resonance imaging; Grading (engineering); Effective diffusion coefficient; Histopathology; Diffusion MRI; Cancer; Cancer detection; Radiology; Modality (human–computer interaction); Prostate; Diffusion-Weighted Magnetic Resonance Imaging; Diffusion imaging; Nuclear medicine; Pathology; Artificial intelligence; Internal medicine; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005296863,0.0006283122,0.0005974583,0.002036675,0.0003024164,0.0009751716,0.0004554444,0.0006456803,0.001712112],"category_scores_gemma":[0.01967598,0.0001900158,0.0007432714,0.001118206,0.0005375458,0.001494137,0.0005439089,0.0003994799,0.0002931086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007264806,"about_ca_system_score_gemma":0.000536964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002796146,"about_ca_topic_score_gemma":0.003334181,"domain_scores_codex":[0.9979067,0.0008804449,0.0001458871,0.0003811547,0.0005950325,0.00009088277],"domain_scores_gemma":[0.9909978,0.005721493,0.0007660192,0.0006034011,0.001548737,0.0003625167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01718029,0.001260623,0.4353283,0.002616913,0.003017648,0.001169645,0.0009978416,0.01299964,0.02383071,0.002114566,0.003500017,0.4959838],"study_design_scores_gemma":[0.0005704585,0.01400104,0.8516532,0.0004748454,0.00405728,0.005988882,0.001118085,0.09391731,0.01471894,0.002529946,0.01067778,0.0002922763],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9418676,0.03926187,0.01365414,0.0004880723,0.0001936758,0.0002421218,0.0007640955,0.0001369784,0.003391486],"genre_scores_gemma":[0.9869735,0.005263913,0.006376522,0.00009491303,0.000167717,0.00005103205,0.0006427404,0.00004283075,0.0003869035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005296863,"threshold_uncertainty_score":0.02801281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01803461854946382,"score_gpt":0.3478507290520844,"score_spread":0.3298161105026206,"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."}}