{"id":"W1974240359","doi":"10.1373/clinchem.2004.032177","title":"How Are We Going to Discover New Cancer Biomarkers? A Proteomic Approach for Bladder Cancer","year":2004,"lang":"en","type":"letter","venue":"Clinical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Cancer; Cancer biomarkers; Disease; Medicine; Biomarker discovery; Population; Biomarker; Bladder cancer; Monoclonal antibody; Computational biology; Identification (biology); Bioinformatics; Immunology; Proteomics; Biology; Internal medicine; Antibody; Gene; Genetics","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":[0.003228799,0.0008624151,0.001407001,0.002553742,0.0009351809,0.002812164,0.001039898,0.002414234,0.002139506],"category_scores_gemma":[0.002639641,0.0004927252,0.001139314,0.001322594,0.002035283,0.006111935,0.001346816,0.00388997,0.0008605038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001842285,"about_ca_system_score_gemma":0.002201931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009192965,"about_ca_topic_score_gemma":0.001344821,"domain_scores_codex":[0.9993931,0.000217662,0.00004684958,0.00007330895,0.0001786088,0.00009052998],"domain_scores_gemma":[0.9991156,0.0002436132,0.00007408782,0.00004854564,0.0002930358,0.0002251623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009255403,0.000477191,0.009022282,0.005160091,0.0004987695,0.002086307,0.0005214008,0.001739885,0.07265998,0.03990798,0.1061356,0.760865],"study_design_scores_gemma":[0.0003657314,0.002197841,0.02163322,0.004710309,0.001221616,0.009023681,0.00253524,0.01019577,0.03086857,0.203193,0.7136312,0.0004237668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"commentary","genre_scores_codex":[0.01720832,0.7221342,0.02068127,0.2287258,0.00550518,0.0001406825,0.0002264779,0.0002430483,0.005135051],"genre_scores_gemma":[0.1301345,0.7462553,0.05495524,0.05240568,0.009910214,0.0003181708,0.0003280709,0.00009253169,0.005600294],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.003228799,"threshold_uncertainty_score":0.01707572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07254487712600402,"score_gpt":0.3744346133324696,"score_spread":0.3018897362064656,"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."}}