{"id":"W2142766872","doi":"10.1373/clinchem.2005.050708","title":"Analytical and Preanalytical Biases in Serum Proteomic Pattern Analysis for Breast Cancer Diagnosis","year":2005,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency","funders":"","keywords":"Breast cancer; Mammography; Medicine; Protein chip; Disease; Cancer; Oncology; Internal medicine; Computational biology; Pathology; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.07351042,0.0007619014,0.0008551112,0.002475319,0.001070819,0.002191193,0.001987239,0.001426068,0.000713844],"category_scores_gemma":[0.1999583,0.0004910252,0.0006858762,0.002227676,0.00206074,0.001061068,0.001642854,0.001089748,0.0003624295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001698743,"about_ca_system_score_gemma":0.002175712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002173627,"about_ca_topic_score_gemma":0.002431215,"domain_scores_codex":[0.9111552,0.03924716,0.007692166,0.006565896,0.03419374,0.001145792],"domain_scores_gemma":[0.8419723,0.1113664,0.02253183,0.009729322,0.01344861,0.000951532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002001021,0.0003744551,0.8887383,0.000924812,0.0008603983,0.0005952778,0.001679523,0.001550351,0.008260335,0.003704344,0.00193099,0.08938015],"study_design_scores_gemma":[0.0002568209,0.004166844,0.8159329,0.001345419,0.001789332,0.009699075,0.001629664,0.03674208,0.07617354,0.01642931,0.03563862,0.0001963446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8822697,0.02991903,0.07268368,0.004761478,0.001187371,0.001426328,0.000702909,0.000233694,0.006815782],"genre_scores_gemma":[0.9552467,0.002911924,0.03659242,0.002790544,0.0008165434,0.0004847447,0.0004018089,0.0000889852,0.0006662428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07351042,"threshold_uncertainty_score":0.3887649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05204954793637195,"score_gpt":0.3954623477659331,"score_spread":0.3434127998295611,"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."}}