{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002745868,0.0001821953,0.0004734267,0.0001622792,0.0001808829,0.00001368281,0.000230086,0.0001374847,0.001362596],"category_scores_gemma":[0.1204153,0.00008971543,0.0000684524,0.0006580917,0.000351971,0.00007970044,0.0001364268,0.0006059348,0.001372578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001035541,"about_ca_system_score_gemma":0.001343486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001623873,"about_ca_topic_score_gemma":0.0001726001,"domain_scores_codex":[0.9963803,0.0001545295,0.0005199382,0.000335458,0.001780144,0.0008295926],"domain_scores_gemma":[0.9913098,0.004251261,0.00008154655,0.0007355913,0.0003142215,0.003307565],"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":[0.0003175878,0.00002878264,0.004647165,0.00003807465,0.00002371935,0.0002315751,0.001378375,0.000001301756,0.00292355,0.00003475956,0.8019181,0.1884569],"study_design_scores_gemma":[0.002529796,0.0007262792,0.05869449,0.001068518,0.00004896017,0.00003352642,0.001434756,0.000346997,0.0007638592,0.002022282,0.932232,0.00009857008],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.4884558,0.004977414,0.0005559539,0.4929461,0.002145315,0.001034067,0.00002926639,0.0004605918,0.009395521],"genre_scores_gemma":[0.851043,0.01122302,0.0009130782,0.1130085,0.0094502,0.0001161197,0.0003355297,0.00008595411,0.01382461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3799376,"threshold_uncertainty_score":0.9995503,"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."}}