{"id":"W4415913729","doi":"10.1016/j.jval.2025.09.3055","title":"Health Economics and Outcomes Research in the New Era of Artificial Intelligence: Catch Me If You Can","year":2025,"lang":"en","type":"editorial","venue":"Value in Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Health economics; Outcomes research; Medical economics; MEDLINE","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":[0.02927091,0.004758677,0.008295583,0.006852382,0.004877127,0.01803139,0.005895516,0.03247395,0.01041714],"category_scores_gemma":[0.1043117,0.001880131,0.004653809,0.004410503,0.006739149,0.01071019,0.002831747,0.04094555,0.006462798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00591358,"about_ca_system_score_gemma":0.008789484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003715688,"about_ca_topic_score_gemma":0.01151739,"domain_scores_codex":[0.9797997,0.005335635,0.002727604,0.001546167,0.00992835,0.0006625595],"domain_scores_gemma":[0.8413815,0.1022156,0.005297541,0.003018546,0.03717653,0.0109101],"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":[0.00002579011,0.00001233935,0.00002821904,0.0004219654,0.00004682847,0.00005851031,0.00002279877,0.00002583336,0.00001755368,0.000788613,0.9943833,0.004168194],"study_design_scores_gemma":[0.0002032486,0.00004719932,0.0004685553,0.003056826,0.0002741969,0.000326666,0.0002334414,0.0004727721,0.00008887841,0.01109,0.9836585,0.00007968346],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00001345842,0.01007857,0.0001131142,0.05269569,0.936589,0.0000101054,0.00003013269,0.00002340253,0.0004465898],"genre_scores_gemma":[0.0001762563,0.004646773,0.0001205643,0.01934834,0.9742038,0.00001460966,0.00001118234,0.00001705484,0.00146146],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03247395,"threshold_uncertainty_score":0.1548012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2437679885706144,"score_gpt":0.5078744946077316,"score_spread":0.2641065060371173,"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."}}