{"id":"W4386003490","doi":"10.1089/ipm.10.04.13","title":"Stories from the Front Lines—Precision Medicine in Practice","year":2023,"lang":"en","type":"article","venue":"Inside Precision Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Workgroup; Precision medicine; Genomics; Medical genetics; Health informatics; Medicine; Medical education; Library science; Computer science; Genetics; Genome; Biology; Nursing; Public health; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03481804,0.001283031,0.001744856,0.001606438,0.008157221,0.01839604,0.002149517,0.01788848,0.01414171],"category_scores_gemma":[0.1117631,0.0008168638,0.0007903561,0.001779292,0.0362197,0.04128167,0.01334985,0.0473427,0.004927738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00585848,"about_ca_system_score_gemma":0.007896103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0026758,"about_ca_topic_score_gemma":0.002656869,"domain_scores_codex":[0.9656236,0.0203628,0.001272722,0.001954938,0.009027456,0.0017585],"domain_scores_gemma":[0.8861477,0.08262254,0.005427377,0.005099561,0.009787071,0.01091574],"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":[0.0001755917,0.00007544631,0.001294013,0.0007679603,0.00009676233,0.001080704,0.01596127,0.0002743252,0.0004933865,0.2823264,0.622803,0.07465114],"study_design_scores_gemma":[0.00006427082,0.00005380249,0.0006927932,0.001762201,0.00003129739,0.001176793,0.01202568,0.000270155,0.0003213032,0.2766009,0.7069246,0.00007622565],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0008831817,0.02914443,0.002180635,0.9527146,0.00533551,0.000008318202,0.00005366255,0.0000404555,0.009639176],"genre_scores_gemma":[0.1240354,0.05261242,0.004661243,0.7703172,0.03450829,0.0001285105,0.0001282678,0.0003370426,0.01327161],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.03481804,"threshold_uncertainty_score":0.1841376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02746550134113333,"score_gpt":0.3295563363277791,"score_spread":0.3020908349866458,"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."}}