{"id":"W4390414658","doi":"10.1089/ipm.10.06.14","title":"Stories <i>from the</i> Front Lines","year":2023,"lang":"en","type":"article","venue":"Inside Precision Medicine","topic":"Health and Medical Research Impacts","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Workgroup; Genomics; Precision medicine; Health informatics; Medical genetics; Library science; Medicine; Medical education; 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":[0.01080867,0.001143159,0.0006490666,0.001847779,0.01080457,0.02045094,0.002051786,0.01389613,0.03191974],"category_scores_gemma":[0.05247718,0.0006088008,0.000842202,0.001987017,0.02170809,0.02978566,0.01069126,0.03251034,0.00953682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004936792,"about_ca_system_score_gemma":0.003397281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004356635,"about_ca_topic_score_gemma":0.005145818,"domain_scores_codex":[0.9866114,0.006985211,0.0002883263,0.0006470422,0.004047818,0.001420272],"domain_scores_gemma":[0.9538015,0.03115277,0.002504922,0.002319765,0.0047301,0.005490892],"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":[0.00003583288,0.00001652735,0.0003043722,0.0001305167,0.00001545675,0.0003961768,0.01599875,0.00003539658,0.0001928485,0.1218254,0.8497534,0.01129535],"study_design_scores_gemma":[0.000007765057,0.000008250259,0.0002111151,0.0002577137,0.000005420589,0.0002917289,0.01507824,0.00003776262,0.0001488628,0.02788112,0.9560513,0.00002078986],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001908533,0.006530799,0.001200113,0.8911837,0.01875345,0.00001226093,0.0001672567,0.00008148268,0.08016241],"genre_scores_gemma":[0.1210449,0.009412246,0.001600328,0.71987,0.03466988,0.0001018551,0.0003024785,0.0008171938,0.1121811],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.03191974,"threshold_uncertainty_score":0.1067822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2524328075995396,"score_gpt":0.4751708387728113,"score_spread":0.2227380311732717,"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."}}