{"id":"W2072218937","doi":"10.1373/clinchem.2007.091470","title":"Analytical Validation of Serum Proteomic Profiling for Diagnosis of Prostate Cancer: Sources of Sample Bias","year":2007,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Health Services","funders":"National Cancer Institute","keywords":"Prostate cancer; Medicine; Prostate; Profiling (computer programming); Oncology; Internal medicine; Cancer; Algorithm; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03873476,0.0008996829,0.0008288271,0.001136067,0.0008659839,0.001305642,0.0009217391,0.0008970824,0.000440086],"category_scores_gemma":[0.06427062,0.000322609,0.0005506785,0.0008428158,0.001757524,0.0003601692,0.0009823724,0.000676652,0.0002713217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008570857,"about_ca_system_score_gemma":0.001519164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00093383,"about_ca_topic_score_gemma":0.0007192258,"domain_scores_codex":[0.956741,0.02010458,0.004413947,0.004098307,0.01402194,0.0006202604],"domain_scores_gemma":[0.9472349,0.0346352,0.00542137,0.004701816,0.007597669,0.0004090542],"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":[0.005074361,0.0008380436,0.6169075,0.0009717718,0.001028568,0.0005933202,0.001606949,0.005788568,0.2767208,0.002562984,0.0007599291,0.0871472],"study_design_scores_gemma":[0.0002910696,0.00325407,0.1418066,0.00021327,0.0008280234,0.003778041,0.0002414726,0.0333103,0.8044502,0.003795173,0.007918552,0.0001134237],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8891212,0.001863269,0.1059192,0.0004530555,0.0001436145,0.0008317996,0.00032502,0.0001715872,0.001171245],"genre_scores_gemma":[0.9428659,0.0003249952,0.05518109,0.0004427765,0.00004492705,0.0003855286,0.0004674509,0.00004198575,0.0002454159],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03873476,"threshold_uncertainty_score":0.2048514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07336208687758376,"score_gpt":0.4028233770017003,"score_spread":0.3294612901241166,"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."}}