{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001000496,0.0006520486,0.003654118,0.0002605474,0.0001607789,0.00002960624,0.0001876227,0.0004387677,0.000178634],"category_scores_gemma":[0.00112934,0.0004216912,0.0007096265,0.0003590249,0.0003636483,0.00006339505,0.0001257274,0.0005304829,0.000011147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007157176,"about_ca_system_score_gemma":0.001478842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002428804,"about_ca_topic_score_gemma":0.0001271173,"domain_scores_codex":[0.9966351,0.0001342893,0.001368901,0.0009521773,0.0003240203,0.0005854486],"domain_scores_gemma":[0.9983318,0.0003663436,0.000653116,0.0002637038,0.0002435673,0.0001414443],"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":[0.0006847762,0.0001968823,0.001184651,0.03597244,0.0006133717,0.00003244084,0.0003957578,1.743358e-8,0.00001138484,0.000009932253,0.0006931252,0.9602053],"study_design_scores_gemma":[0.01025413,0.006763436,0.001045353,0.06643535,0.005229746,0.00009684413,0.0001645643,0.00009245191,0.0000470938,0.0001037106,0.9086733,0.001093987],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003951104,0.9750616,0.0000325378,0.0003175095,0.0005704389,0.01957011,0.0001174254,0.00008029005,0.000298979],"genre_scores_gemma":[0.0001526229,0.9735225,0.003767638,0.00007546191,0.002442916,0.01923157,0.00008782964,0.0001165658,0.0006028441],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9591112,"threshold_uncertainty_score":0.9998235,"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."}}