{"id":"W4408277156","doi":"10.2196/64473","title":"Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study","year":2025,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Health Promotion and Cardiovascular Prevention","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Computer science; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001171281,0.000105488,0.000266156,0.0001465809,0.0002453038,0.00002970456,0.00006048549,0.00006191144,0.000006801105],"category_scores_gemma":[0.00002770383,0.00009618961,0.00003737353,0.0001997123,0.00002975884,0.0001080633,0.0001419943,0.0001789147,0.00000358239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001711767,"about_ca_system_score_gemma":0.0002484382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002083839,"about_ca_topic_score_gemma":0.000006206647,"domain_scores_codex":[0.9984515,0.0003548966,0.0004441944,0.000374123,0.0001926982,0.0001826046],"domain_scores_gemma":[0.9994741,0.00001671039,0.00007797097,0.0002743215,0.00006945734,0.00008741744],"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":[0.00006119672,0.001047969,0.02239932,0.0007849615,0.0003821173,0.00003306199,0.006388224,0.005240176,0.002217476,0.0002519067,0.0001818057,0.9610118],"study_design_scores_gemma":[0.001441957,0.0004715865,0.2923035,0.001339483,0.0002249339,0.00006051931,0.008607187,0.6897164,0.002076679,0.000819487,0.00256458,0.0003736918],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6299052,0.0001399954,0.3683805,0.0002658376,0.0001050378,0.001090155,0.000003780707,0.00006354332,0.00004591852],"genre_scores_gemma":[0.9912354,0.00005793788,0.008057351,0.0002528262,0.00007375822,0.0000447853,0.000231368,0.00000706341,0.00003948058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9606381,"threshold_uncertainty_score":0.3922499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2676205564612297,"score_gpt":0.445163564099685,"score_spread":0.1775430076384553,"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."}}