{"id":"W4411574638","doi":"10.1017/s0266462325100111","title":"Adopting life-cycle HTA: a tumor-agnostic precision oncology index economic evaluation from publicly available reimbursement reviews","year":2025,"lang":"en","type":"article","venue":"International Journal of Technology Assessment in Health Care","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia; Spinal Cord Injury BC","funders":"AstraZeneca","keywords":"Reimbursement; Index (typography); Medicine; Precision oncology; Oncology; Medical physics; Internal medicine; Computer science; Economics; Cancer; Economic growth; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.1047728,0.002239208,0.002561217,0.006081841,0.0005612604,0.008625585,0.002953537,0.002132253,0.007809739],"category_scores_gemma":[0.3267592,0.001155525,0.006723155,0.006681259,0.0008169073,0.003073309,0.002722485,0.002870454,0.001096656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01521096,"about_ca_system_score_gemma":0.03711296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03379018,"about_ca_topic_score_gemma":0.03062089,"domain_scores_codex":[0.8630902,0.1059416,0.008640825,0.003350392,0.01749746,0.001479443],"domain_scores_gemma":[0.8047476,0.1338753,0.01813208,0.01190247,0.03007195,0.001270564],"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":[0.004491178,0.0005773039,0.02230546,0.03398335,0.02348198,0.0002455336,0.0007857213,0.2082554,0.0006475491,0.07434171,0.0744088,0.556476],"study_design_scores_gemma":[0.007628481,0.003636225,0.0430439,0.05214702,0.02836871,0.0004871025,0.0005898839,0.4076378,0.005770733,0.1486582,0.3009554,0.001076455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06835551,0.06230832,0.5489821,0.02581066,0.00160913,0.04714892,0.113011,0.003638852,0.1291356],"genre_scores_gemma":[0.5672188,0.01530202,0.3415975,0.006055398,0.0006500744,0.03073152,0.03390336,0.0008519676,0.003689363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1047728,"threshold_uncertainty_score":0.5540982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1864393731191579,"score_gpt":0.5042679457208976,"score_spread":0.3178285726017397,"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."}}