{"id":"W2903169604","doi":"10.3390/proteomes6040049","title":"Clinical Proteomics in Colorectal Cancer, a Promising Tool for Improving Personalised Medicine","year":2018,"lang":"en","type":"review","venue":"Proteomes","topic":"Colorectal Cancer Treatments and Studies","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Cancer Research Society","keywords":"Colorectal cancer; Medicine; Biomarker; Cancer; Biomarker discovery; Proteomics; Clinical Practice; Oncology; Intensive care medicine; Internal medicine; Bioinformatics; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001140364,0.0006964828,0.00129649,0.00304328,0.0003091089,0.001514654,0.000750422,0.001415166,0.00460321],"category_scores_gemma":[0.001867034,0.0002265661,0.0006211533,0.002665003,0.000602608,0.00177535,0.0009643073,0.001903477,0.0025675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007452452,"about_ca_system_score_gemma":0.001437655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008792962,"about_ca_topic_score_gemma":0.001253515,"domain_scores_codex":[0.9995766,0.0001157559,0.00006203292,0.0000609264,0.0001510809,0.00003350365],"domain_scores_gemma":[0.9989657,0.0006155692,0.0001172751,0.00003597967,0.0002085578,0.00005686324],"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":[0.00006746686,0.00005644184,0.0002613007,0.02945353,0.0001513641,0.0001799521,0.0000925771,0.0002063161,0.001451745,0.008266047,0.02941919,0.930394],"study_design_scores_gemma":[0.00001093577,0.00007810036,0.0007661399,0.00689664,0.0001064836,0.0008606503,0.0000854595,0.00007427272,0.0004162168,0.003336497,0.9873492,0.00001945886],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007224263,0.9981043,0.0001668492,0.0005255365,0.0001918605,0.000005662788,0.00002344284,0.000008649094,0.0009013023],"genre_scores_gemma":[0.0005422327,0.99839,0.0002433875,0.0002503239,0.000167648,0.000006779745,0.00002805045,0.00000178015,0.0003698699],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00460321,"threshold_uncertainty_score":0.01539928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.107268841060418,"score_gpt":0.4424170369809506,"score_spread":0.3351481959205326,"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."}}