{"id":"W4413051032","doi":"10.3233/shti251009","title":"Digital Twins of Patients: A Cohort Matching Interpretation","year":2025,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Personalization; Matching (statistics); Context (archaeology); Computer science; Cohort; Precision medicine; Field (mathematics); Health care; Interpretation (philosophy); Medical record; Data science; Medicine; World Wide Web; Surgery; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0001094053,0.00007944162,0.000205333,0.000488897,0.00003868499,0.000009605546,0.00007270234,0.00009372564,6.135479e-7],"category_scores_gemma":[0.00008956903,0.00007731918,0.00001131228,0.0004608497,0.0001624088,0.00047607,0.0000502405,0.0001932232,0.000001586029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008296158,"about_ca_system_score_gemma":0.00001738365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.833864e-7,"about_ca_topic_score_gemma":0.000001921416,"domain_scores_codex":[0.9990885,0.000003016146,0.0006850665,0.00003382614,0.00005490029,0.0001346789],"domain_scores_gemma":[0.9997253,0.00005506718,0.00006877009,0.00008855581,0.00005059972,0.00001164541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002256016,0.0000728658,0.4549524,0.01174798,0.0003051537,3.827891e-7,0.02094229,0.006534346,4.558787e-7,0.08169876,0.001674272,0.4220485],"study_design_scores_gemma":[0.007610627,0.00133538,0.2209951,0.01658918,0.00008412161,0.00002060293,0.3877183,0.09623475,0.0009102166,0.2016253,0.06498631,0.001890116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9164757,0.001229269,0.01254918,0.000320654,0.0005663756,0.0004981507,0.00004408193,0.0003219657,0.06799463],"genre_scores_gemma":[0.9985537,0.0006843345,0.0006154671,0.00008901836,0.000001568042,0.00002629656,0.000008203769,0.000003505892,0.000017914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4201584,"threshold_uncertainty_score":0.3152985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01173250555089491,"score_gpt":0.2858702249259243,"score_spread":0.2741377193750294,"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."}}