{"id":"W7067685948","doi":"","title":"Molecular imaging targets in prostate cancers with neuroendocrine gene signature","year":2019,"lang":"en","type":"dissertation","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Korea Health Industry Development Institute; National Research Foundation of Korea; Ministry of Education, Science and Technology; National Research Foundation; Government of Ontario","keywords":"Prostate cancer; Prostate; Molecular imaging; Phenotype; Transmembrane protein; Lineage (genetic); Cancer; Neuroendocrine differentiation; Neuroendocrine tumors; Gene signature","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":[0.0001553967,0.0002028881,0.0003090126,0.000313085,0.00009469954,0.0004109102,0.0001746492,0.0003100008,0.001270521],"category_scores_gemma":[0.0001933018,0.0001300846,0.0002121829,0.0003109329,0.0001282791,0.0002515938,0.0001654018,0.0003669389,0.0002470531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477472,"about_ca_system_score_gemma":0.000187531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006107521,"about_ca_topic_score_gemma":0.001024748,"domain_scores_codex":[0.9998894,0.00002299449,0.000005334276,0.00002076591,0.00003850235,0.00002304703],"domain_scores_gemma":[0.9999366,0.00001431951,0.00001965658,0.000004142669,0.00001295686,0.00001227484],"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":[0.0004731953,0.0001358555,0.008021479,0.0004528477,0.00003412255,0.0004209517,0.0000645756,0.0009177326,0.9582692,0.0004907153,0.000374698,0.03034465],"study_design_scores_gemma":[0.00006873342,0.003833973,0.06877431,0.0000704093,0.0002308905,0.004314194,0.0002880382,0.006812391,0.8854414,0.000805221,0.02932131,0.00003913101],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544796,0.03617689,0.00485365,0.0003294252,0.00005626774,0.00008307003,0.0003857871,0.0001165521,0.003518742],"genre_scores_gemma":[0.9831536,0.009152888,0.004567996,0.0001797087,0.00002493668,0.00006065715,0.0005700117,0.00002526869,0.00226495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001270521,"threshold_uncertainty_score":0.004250348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008813353077735226,"score_gpt":0.2506397717144768,"score_spread":0.2418264186367415,"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."}}