{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01380133,0.0003488965,0.0005023384,0.003609962,0.001131823,0.002539658,0.00137495,0.0009860336,0.00895617],"category_scores_gemma":[0.05052468,0.0002564758,0.001086169,0.003803679,0.0009255339,0.002181056,0.002447831,0.001045722,0.0009124111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008553345,"about_ca_system_score_gemma":0.001843408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005772231,"about_ca_topic_score_gemma":0.003384747,"domain_scores_codex":[0.9937704,0.002944875,0.0006267484,0.001309117,0.00106701,0.000281693],"domain_scores_gemma":[0.9823225,0.009311788,0.001568109,0.004498953,0.001855526,0.0004431211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001576328,0.0003201829,0.4026005,0.0004404766,0.0004970322,0.003876199,0.005744709,0.00509925,0.003303141,0.1352219,0.02393459,0.4173857],"study_design_scores_gemma":[0.0003152583,0.0009550402,0.1627862,0.001148159,0.001631481,0.01580087,0.01451409,0.1370069,0.01183165,0.3866401,0.2671595,0.0002107664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2776975,0.001755354,0.6730185,0.007271444,0.001061172,0.001951544,0.01263381,0.0008821937,0.02372838],"genre_scores_gemma":[0.6965504,0.0007590415,0.288296,0.001246476,0.0002831048,0.0007118532,0.005416212,0.0001838644,0.006552979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01380133,"threshold_uncertainty_score":0.07298923,"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."}}