{"id":"W2993527780","doi":"10.1159/000504652","title":"How to Integrate Personalized Medicine into Prevention? Recommendations from the Personalized Prevention of Chronic Diseases (PRECeDI) Consortium","year":2019,"lang":"en","type":"article","venue":"Public Health Genomics","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre","funders":"Università Cattolica del Sacro Cuore; Sapienza Università di Roma; European Commission","keywords":"Personalized medicine; Medicine; Identification (biology); Cancer prevention; Disease; Translational research; Medical education; Cancer; Bioinformatics; Pathology","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":[0.12429,0.002195153,0.002788103,0.006117103,0.003186296,0.01072467,0.008923049,0.0302517,0.01127999],"category_scores_gemma":[0.1752075,0.001104366,0.00537163,0.006141808,0.0047711,0.01022507,0.01302368,0.03586357,0.007170609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008571166,"about_ca_system_score_gemma":0.1013052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0303658,"about_ca_topic_score_gemma":0.04514569,"domain_scores_codex":[0.9254412,0.03901088,0.009875133,0.003053121,0.01719927,0.005420299],"domain_scores_gemma":[0.767912,0.1077098,0.01075956,0.006854821,0.06391508,0.04284879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001344653,0.0002731202,0.0009126168,0.002529938,0.00009395752,0.000201238,0.0006355517,0.000387719,0.0001964764,0.01460067,0.8741176,0.1059166],"study_design_scores_gemma":[0.0003448438,0.0001241541,0.002792874,0.01743692,0.0002863164,0.0001093455,0.001029742,0.0003870215,0.0002948702,0.01162123,0.9654065,0.0001661647],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.0003345333,0.0208232,0.003007171,0.9524335,0.01318417,0.0006663495,0.0007944105,0.0002100755,0.008546576],"genre_scores_gemma":[0.006557658,0.09634463,0.06689812,0.7900159,0.01400135,0.00449602,0.004658738,0.0003266599,0.01670089],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.12429,"threshold_uncertainty_score":0.6573161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02869117607280319,"score_gpt":0.3183605289377063,"score_spread":0.2896693528649031,"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."}}