{"id":"W4280547561","doi":"10.4155/bio-2022-0078","title":"2021 White Paper on Recent Issues in Bioanalysis: Mass Spec of Proteins, Extracellular Vesicles, CRISPR, Chiral Assays, Oligos; Nanomedicines Bioanalysis; ICH M10 Section 7.1; Non-Liquid &amp; Rare Matrices; Regulatory Inputs ( <u>Part 1A</u> – Recommendations on Endogenous Compounds, Small Molecules, Complex Methods, Regulated Mass Spec of Large Molecules, Small Molecule, PoC &amp; <u>Part 1B</u> - Regulatory Agencies' Inputs on Bioanalysis, Biomarkers, Immunogenicity, Gene &amp; Cell Therapy and Vaccine)","year":2022,"lang":"en","type":"article","venue":"Bioanalysis","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; Seagen (Canada)","funders":"World Health Organization","keywords":"Bioanalysis; White paper; Excellence; Computer science; Computational biology; Political science; Nanotechnology; 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","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0062616,0.001881995,0.003990964,0.004555536,0.001078172,0.0001393299,0.001695062,0.001176622,0.00412039],"category_scores_gemma":[0.0002898822,0.001749628,0.00187872,0.0076551,0.0007474558,0.0001999902,0.0007125966,0.001682462,0.00004653868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055821,"about_ca_system_score_gemma":0.0003729105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001436041,"about_ca_topic_score_gemma":0.001872672,"domain_scores_codex":[0.9825138,0.007349921,0.004280774,0.003036209,0.001053741,0.001765544],"domain_scores_gemma":[0.9913266,0.0006026433,0.00311824,0.003692196,0.0008703858,0.0003899656],"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":[0.004145313,0.003786524,0.001839095,0.0002223084,0.01150564,0.00006240229,0.0004439041,0.001105203,0.9692112,0.0001839873,0.003726997,0.003767477],"study_design_scores_gemma":[0.008578439,0.002621196,0.003509526,0.0004414726,0.007682602,0.00008346827,0.001164371,0.001086878,0.6097897,0.000221472,0.3617112,0.003109712],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6973453,0.2580279,0.0153259,0.01270651,0.002149518,0.00606329,0.005610672,0.0004690814,0.002301894],"genre_scores_gemma":[0.7812488,0.0660162,0.1161487,0.00382025,0.0006875069,0.0007189095,0.02066904,0.0006950127,0.009995597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3594215,"threshold_uncertainty_score":0.9993924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04661098129259345,"score_gpt":0.2895291604763159,"score_spread":0.2429181791837224,"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."}}