{"id":"W2333875338","doi":"10.1515/cclm.2011.683","title":"Multiplex protein assay performance/evaluation and the requirement for precision and correlation to clinical assays","year":2011,"lang":"en","type":"letter","venue":"Clinical Chemistry and Laboratory Medicine (CCLM)","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research; McMaster University","keywords":"Multiplex; Correlation; Computational biology; Chromatography; Statistics; Computer science; Medicine; Biology; Chemistry; Mathematics; Bioinformatics","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":[],"consensus_categories":[],"category_scores_codex":[0.005010094,0.0003046545,0.0005030429,0.00001587932,0.0002070555,0.00002135859,0.0001695652,0.001090839,0.00001281482],"category_scores_gemma":[0.003470263,0.0002066459,0.0000783418,0.00007890131,0.0009217569,0.000008185407,0.0001793528,0.0008125306,9.863616e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001562238,"about_ca_system_score_gemma":0.0001153697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003178337,"about_ca_topic_score_gemma":0.000001656634,"domain_scores_codex":[0.9974444,0.0003088742,0.0009364899,0.0008388026,0.0002460219,0.0002254289],"domain_scores_gemma":[0.997843,0.0004884892,0.0004551557,0.0005766694,0.0004813869,0.0001552525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001941906,0.0001556551,0.003026084,0.0006547375,0.000234141,0.000004378458,0.0001014486,0.000003445991,0.03830541,0.00005600955,0.8349462,0.1205705],"study_design_scores_gemma":[0.004267525,0.0008924223,0.001440269,0.0004557592,0.0003392105,0.000009669457,0.00003863464,0.001306328,0.00992672,0.0006258411,0.9802499,0.0004476892],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6604573,0.005521154,0.03649071,0.282733,0.001052139,0.01177419,0.0003483025,0.0001555731,0.00146762],"genre_scores_gemma":[0.6519162,0.009722275,0.02943311,0.266753,0.02982403,0.002875707,0.004504915,0.0002725281,0.004698244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1453037,"threshold_uncertainty_score":0.8426777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07199597884356118,"score_gpt":0.3915731722650142,"score_spread":0.319577193421453,"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."}}