{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004135712,0.0008515391,0.001829184,0.003729987,0.0003560265,0.0001258407,0.0003719646,0.0009274227,0.00008961432],"category_scores_gemma":[0.001313208,0.0005916744,0.00064495,0.004295386,0.001038557,0.0003659938,0.0001354343,0.0005021775,0.000001104165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004562866,"about_ca_system_score_gemma":0.0002281454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003907132,"about_ca_topic_score_gemma":0.001652139,"domain_scores_codex":[0.9949092,0.0009363802,0.001673948,0.001420496,0.0002230811,0.0008369256],"domain_scores_gemma":[0.9935567,0.004281252,0.0007226252,0.0008158202,0.0005248442,0.00009876103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.02032369,0.0041201,0.1900411,0.0006156734,0.03229672,0.00001652597,0.002089588,0.001189967,0.5611726,0.001895194,0.008169224,0.1780696],"study_design_scores_gemma":[0.02222916,0.004773846,0.03999445,0.001829202,0.006662318,0.000008513672,0.02058629,0.05732837,0.8275434,0.003826219,0.01257769,0.002640521],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952329,0.01247704,0.01326097,0.0122384,0.000366482,0.003695737,0.005353945,0.00008085872,0.0001976004],"genre_scores_gemma":[0.9847885,0.002226294,0.01099666,0.0004211081,0.00001767389,0.0004017725,0.0008352857,0.00005223884,0.0002604292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2663708,"threshold_uncertainty_score":0.9996535,"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."}}