{"id":"W2420725116","doi":"10.1089/humc.2016.29012.kod","title":"Sequenom, the U.S. Supreme Court, and Personalized Medicine","year":2016,"lang":"en","type":"editorial","venue":"Human Gene Therapy Clinical Development","topic":"Biomedical Ethics and Regulation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dawson College","funders":"","keywords":"Supreme court; Personalized medicine; Law; Political science; Library science; Computer science; Bioinformatics; 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":["sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004572698,0.0004872148,0.001164154,0.0001115443,0.0004207825,0.00003289786,0.0003329251,0.001710524,0.001302653],"category_scores_gemma":[0.0004320685,0.0002242214,0.0002028196,0.0001073069,0.003805175,0.00002561716,0.0001445838,0.001553702,0.00004844083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001705896,"about_ca_system_score_gemma":0.001561576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002258596,"about_ca_topic_score_gemma":0.00001872099,"domain_scores_codex":[0.9951843,0.0003369166,0.001520381,0.0008013283,0.001735072,0.0004220037],"domain_scores_gemma":[0.9963883,0.001607614,0.0004811943,0.0006010859,0.0004920792,0.0004297025],"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":[0.0004687199,0.0001380646,0.00008039393,0.00009853199,0.0006700399,0.00003262187,0.0008138546,9.670844e-9,0.0006952654,0.0004509463,0.9251804,0.0713711],"study_design_scores_gemma":[0.006939513,0.0006945298,0.005006795,0.0006742188,0.0001188297,0.00001249195,0.0000229334,0.000002179857,0.00006102375,0.002330111,0.9838127,0.0003246422],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.01006123,0.02061573,0.0004394003,0.07903447,0.8846262,0.00243274,0.00005796178,0.0002419031,0.002490358],"genre_scores_gemma":[0.002993281,0.03478916,0.0008519542,0.003564875,0.9376875,0.00009670007,0.0009231646,0.0001357934,0.01895764],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.07546959,"threshold_uncertainty_score":0.9996103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1068672157951746,"score_gpt":0.420018691384724,"score_spread":0.3131514755895494,"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."}}