{"id":"W2085448923","doi":"10.1038/modpathol.2013.81","title":"Next-generation biobanking of metastases to enable multidimensional molecular profiling in personalized medicine","year":2013,"lang":"en","type":"article","venue":"Modern Pathology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"St Mary's Hospital Centre; Hôpital du Sacré-Cœur de Montréal; Université de Sherbrooke; Université Laval; McGill University Health Centre; Hôpital du Saint-Sacrement; Hôpital Charles-Le Moyne; Hôtel-Dieu de Québec; McGill University; Jewish General Hospital; Quebec - Clinical Research Organization in Cancer","funders":"Canadian Institutes of Health Research; Sanofi; Pfizer","keywords":"Biobank; Context (archaeology); Liquid biopsy; Medicine; Personalized medicine; Computational biology; Pathology; Bioinformatics; Computer science; Cancer; Biology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000189229,0.0001194656,0.0002052005,0.00009477422,0.00002606667,0.000006093685,0.00008593584,0.00009596096,0.00004632429],"category_scores_gemma":[0.0002096655,0.0001120969,0.00004218619,0.00007630231,0.0000746899,0.000004153278,0.00009201161,0.00005173496,0.000006438265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001743556,"about_ca_system_score_gemma":0.00005485247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000158661,"about_ca_topic_score_gemma":0.00008848964,"domain_scores_codex":[0.9990606,0.00006715624,0.0002527632,0.000323174,0.00009270578,0.0002035622],"domain_scores_gemma":[0.9995229,0.00002004035,0.00006824071,0.0002160432,0.0001153651,0.00005738547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002896994,0.00004121412,0.0009871335,0.000009769429,0.00001189657,0.0000137138,0.0001434871,0.003329196,0.992681,0.0006709184,0.0002549569,0.0018278],"study_design_scores_gemma":[0.001034451,0.0003405251,0.000482711,0.00001908325,0.00001863044,0.00002458512,0.00008922132,0.008203678,0.9876229,0.0008485219,0.001143894,0.0001717713],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761818,0.002044425,0.0208426,0.0003632899,0.0001125819,0.0003019847,0.00002462354,0.000004211532,0.0001245054],"genre_scores_gemma":[0.9849643,0.00009363978,0.01361918,0.0008810803,0.0001070024,0.0000770284,0.0001934602,0.00001717722,0.00004713609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008782516,"threshold_uncertainty_score":0.4571179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984984308680051,"score_gpt":0.2698879980468967,"score_spread":0.2400381549600962,"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."}}