{"id":"W7036177636","doi":"","title":"Automatic image quantification strategies in clinical nuclear medicine and neuroradiology","year":2017,"lang":"ca","type":"dissertation","venue":"Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona)","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institute on Aging; Plan Nacional sobre Drogas; National Institutes of Health; Genentech; IXICO; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas; Servier; Eisai; Medpace; Svenska Forskningsrådet Formas; Pfizer; Novartis Pharmaceuticals Corporation; Synarc; U.S. Department of Defense; Meso Scale Diagnostics; BioClinica; Bristol-Myers Squibb; Eli Lilly and Company; Biogen","keywords":"Neuroradiology; Nuclear imaging; Image processing; Medical imaging; Nuclear medicine imaging; Radionuclide imaging","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.002963512,0.001145286,0.0009503202,0.003306622,0.0006792271,0.003728437,0.001567205,0.001811807,0.005204401],"category_scores_gemma":[0.006394855,0.0009809554,0.001008861,0.002003931,0.0009153622,0.002013058,0.001803943,0.001143177,0.002080927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001082703,"about_ca_system_score_gemma":0.001758897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002934257,"about_ca_topic_score_gemma":0.004165728,"domain_scores_codex":[0.9987814,0.0004249346,0.0001010206,0.0002335555,0.0003515338,0.0001076624],"domain_scores_gemma":[0.997519,0.001144192,0.0001741677,0.0002297929,0.0008519827,0.00008078523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001827004,0.0001254431,0.001528307,0.000411986,0.000109924,0.00009953959,0.0002001201,0.02356074,0.05785184,0.01507147,0.004884858,0.8959731],"study_design_scores_gemma":[0.00004152097,0.0002070368,0.00772361,0.0002741759,0.0002389056,0.001184545,0.0002915135,0.8006217,0.119443,0.04007963,0.02976356,0.0001308491],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009952813,0.002633723,0.9822907,0.0005366074,0.0001052219,0.0001173278,0.0001605989,0.001111734,0.003091318],"genre_scores_gemma":[0.1157575,0.002441859,0.873909,0.0003149318,0.0001438065,0.0001860648,0.0004612167,0.0006563067,0.006129397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005204401,"threshold_uncertainty_score":0.01741046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02479165376624291,"score_gpt":0.33039555477216,"score_spread":0.3056039010059171,"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."}}