{"id":"W1980642039","doi":"10.1016/j.mpdhp.2013.11.009","title":"High-quality biobanking for personalized precision medicine: BioSpecimen Sciences at the helm","year":2013,"lang":"en","type":"article","venue":"Diagnostic histopathology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Biobank; Personalized medicine; Precision medicine; Quality (philosophy); Key (lock); Data science; Biorepository; Computer science; Medicine; Medical physics; Bioinformatics; Pathology; Biology","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":[0.01799712,0.0007875953,0.001280753,0.004071702,0.002225016,0.007034498,0.002278716,0.003285549,0.01545432],"category_scores_gemma":[0.02048765,0.0007344186,0.0005943343,0.003006649,0.003290318,0.006773818,0.01029872,0.005020692,0.009187241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003932368,"about_ca_system_score_gemma":0.01145338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002678277,"about_ca_topic_score_gemma":0.003263375,"domain_scores_codex":[0.9919896,0.002897651,0.0004027735,0.001081596,0.002719744,0.0009086574],"domain_scores_gemma":[0.9780219,0.005219636,0.001646976,0.004925016,0.006510564,0.003675966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005795106,0.0002569148,0.02504695,0.0009936568,0.0002014856,0.0009060986,0.001834681,0.002297346,0.04905578,0.1126203,0.174362,0.6318454],"study_design_scores_gemma":[0.0001107296,0.0002614634,0.01796001,0.00145245,0.000107661,0.002653623,0.002052297,0.004385686,0.03540809,0.205739,0.7296819,0.0001869962],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.07631474,0.06292108,0.3751476,0.379546,0.01130738,0.001234685,0.005043539,0.004582688,0.08390234],"genre_scores_gemma":[0.4074204,0.0393061,0.4131162,0.05596605,0.01409667,0.0009571975,0.003931185,0.002314706,0.06289156],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01799712,"threshold_uncertainty_score":0.09517908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084216662504275,"score_gpt":0.2969835907579599,"score_spread":0.2761414241329172,"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."}}