{"id":"W2056297313","doi":"10.1586/erp.12.15","title":"Personalized medicine policy challenges: measuring clinical utility at point of care","year":2012,"lang":"en","type":"review","venue":"Expert Review of Pharmacoeconomics & Outcomes Research","topic":"Pharmacogenetics and Drug Metabolism","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Personalized medicine; Pharmacogenomics; Precision medicine; Relevance (law); Point of care; Tipping point (physics); Medicine; Scale (ratio); MEDLINE; Data science; Computer science; Bioinformatics; Nursing; 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":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.02274965,0.001344386,0.009654071,0.001163991,0.0003456266,0.00001218406,0.002788323,0.001167583,0.01075031],"category_scores_gemma":[0.001730748,0.001048477,0.003383236,0.0007412602,0.002299896,0.0001861387,0.00194881,0.004010692,0.0004600182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009735479,"about_ca_system_score_gemma":0.001890148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001285343,"about_ca_topic_score_gemma":0.0000115696,"domain_scores_codex":[0.9788394,0.0108583,0.005830639,0.001457456,0.001166845,0.001847388],"domain_scores_gemma":[0.9874051,0.005791685,0.002446225,0.001626555,0.001215143,0.001515285],"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":[0.000138434,0.0004667167,0.0001104925,0.1679966,0.001933013,0.00001179372,0.001545924,2.569823e-7,0.0000346504,0.0006023335,0.007650633,0.8195092],"study_design_scores_gemma":[0.002409579,0.0001279675,0.0000249339,0.01783318,0.002341222,0.00003635748,0.0002213074,0.00001437168,0.000201501,0.0000354995,0.9760245,0.0007295576],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006062696,0.9741505,0.00000214231,0.002103742,0.003500061,0.003909801,0.0006191073,0.00005123104,0.01560282],"genre_scores_gemma":[0.0002436923,0.9921699,0.0001457381,0.002340208,0.003327664,0.0006222203,0.0002312261,0.0002208942,0.0006984208],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9683739,"threshold_uncertainty_score":0.9999307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5349782942177395,"score_gpt":0.68611998141965,"score_spread":0.1511416872019106,"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."}}