{"id":"W3124855862","doi":"10.25916/sut.26244503","title":"Precision medicine: drowning in a regulatory soup?","year":2024,"lang":"en","type":"article","venue":"Own your potential (DEAKIN)","topic":"Biomedical Ethics and Regulation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Australian Research Council","keywords":"Precision medicine; Pharmacogenomics; Biobank; Personalized medicine; Confusion; Health care; Healthcare industry; Risk analysis (engineering); Regulatory science; Business; Medicine; Political science; Law; Bioinformatics; Psychology; Biology; Pharmacology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00094608,0.0001997239,0.0003480105,0.0004612336,0.00008330662,0.00004374568,0.0001163825,0.0004344402,0.0009239113],"category_scores_gemma":[0.0001269037,0.0001399265,0.0001446401,0.0006285503,0.0004550888,0.0001308359,0.00008176722,0.0006944159,0.0001227913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001164944,"about_ca_system_score_gemma":0.0002037998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002260069,"about_ca_topic_score_gemma":0.00002363924,"domain_scores_codex":[0.9977376,0.00006928031,0.0005035755,0.0004757615,0.0008652252,0.0003485721],"domain_scores_gemma":[0.9992394,0.00006747014,0.000063577,0.000319584,0.000083481,0.0002264154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006439377,0.0004056182,0.001066627,0.001492741,0.0002890455,0.002803107,0.003180596,0.0002366118,0.2195706,0.07364377,0.02170027,0.6749671],"study_design_scores_gemma":[0.008622566,0.001611096,0.2330787,0.01423758,0.0008516061,0.00158315,0.0007226272,0.13761,0.003703734,0.04641989,0.5502492,0.001309886],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9142565,0.00456764,0.01131163,0.05582209,0.004443809,0.0007657181,0.000009109532,0.000440787,0.008382773],"genre_scores_gemma":[0.9946592,0.00008824668,0.0005113773,0.0004732498,0.001434248,0.00001124057,0.00006351704,0.00004322141,0.002715728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6736572,"threshold_uncertainty_score":0.9999894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02118375682322231,"score_gpt":0.3165284606024705,"score_spread":0.2953447037792482,"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."}}