{"id":"W4406824892","doi":"10.1080/17576180.2024.2442218","title":"2024 White paper on recent issues in bioanalysis: Impact of LDT in US and IVDR in EU; AI/ML for High Parameter Flow Cytometry; The rise of Olink Technology; CDx for AAV Gene Therapies; Integrative Bioanalysis by Multiple Platforms; Super Sensitive ADA/NAb LBA ( <u>PART 2A</u> – Recommendations on Advanced Strategies for Biomarkers, IVD/CDx Assays (BAV), Cell Based Assays (CBA), and Ligand-Binding Assays (LBA) <u>PART 2B</u> – Regulatory Agencies’ Input on Biomarkers, IVD/CDx, and Biomarker Assay Validation)","year":2025,"lang":"en","type":"article","venue":"Bioanalysis","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pfizer (Canada)","funders":"","keywords":"Bioanalysis; Flow cytometry; Nanotechnology; Chemistry; Chromatography; Materials science; Molecular biology; Biology","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":[0.02247297,0.001589777,0.001107078,0.001781133,0.002397621,0.01113971,0.004490952,0.01752265,0.05542792],"category_scores_gemma":[0.01829667,0.001000803,0.001653939,0.001751554,0.002758172,0.004320825,0.003433195,0.008938741,0.04134055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00528683,"about_ca_system_score_gemma":0.01511302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008467433,"about_ca_topic_score_gemma":0.012727,"domain_scores_codex":[0.9842482,0.002236191,0.001118773,0.001670565,0.009393892,0.00133235],"domain_scores_gemma":[0.9720888,0.005161872,0.002472276,0.001164544,0.0150179,0.004094735],"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":[0.00008610189,0.00006989094,0.0001600288,0.0002455684,0.000009070175,0.0001483079,0.0000588511,0.0001514131,0.001990004,0.003937505,0.9545839,0.03855932],"study_design_scores_gemma":[0.000007787099,0.00003225392,0.0003239559,0.0001349588,0.000003828416,0.00004816979,0.00003413317,0.00005199984,0.0007375642,0.0005870448,0.9980292,0.000009100335],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00344719,0.0587028,0.01281114,0.3266537,0.2470627,0.001123848,0.00493172,0.001856185,0.3434108],"genre_scores_gemma":[0.01038254,0.0179191,0.01064624,0.1307532,0.03652756,0.0005445527,0.004038453,0.001638438,0.78755],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.05542792,"threshold_uncertainty_score":0.1854249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01702756591390475,"score_gpt":0.3036685285899941,"score_spread":0.2866409626760894,"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."}}