{"id":"W4402533942","doi":"10.2147/ceor.s476426","title":"Adjusting Historical Costs for Inflation with the Use of Standardized Automated Tools","year":2024,"lang":"en","type":"article","venue":"ClinicoEconomics and Outcomes Research","topic":"Economic, financial, and policy analysis","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université Laval; Centre hospitalier de l'Université Laval","funders":"Fondation des pompiers du Québec pour les grands brûlés; Réseau Québécois de Recherche sur les Médicaments","keywords":"Medicine; Inflation (cosmology); Data science; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003096858,0.0001608516,0.0006912487,0.0004367839,0.0002556203,0.0004554491,0.0002304297,0.0001469246,0.00004935492],"category_scores_gemma":[0.0009132575,0.0001286325,0.000241617,0.000256514,0.0001801748,0.0004792637,0.00009799068,0.0003016199,0.00004695235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005232035,"about_ca_system_score_gemma":0.0001197707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006914617,"about_ca_topic_score_gemma":0.0003166187,"domain_scores_codex":[0.998046,0.00005582225,0.0009827419,0.0004820864,0.00004509202,0.0003882264],"domain_scores_gemma":[0.9949903,0.004147287,0.0002691497,0.0003841466,0.0001165063,0.00009258075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005119862,0.0001106424,0.2429196,0.0004801798,0.001034631,0.0000055078,0.001675476,0.001752503,0.00002357395,0.6246167,0.04352864,0.08334054],"study_design_scores_gemma":[0.001390555,0.0003892723,0.05117838,0.00005493319,0.00005431957,0.000002820687,0.0001456018,0.1958485,0.00003328675,0.006535353,0.7439904,0.0003765361],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813513,0.002386166,0.003817309,0.008153231,0.0004902187,0.000862813,0.000957078,0.0001061687,0.0018757],"genre_scores_gemma":[0.9937114,0.0009703042,0.001603745,0.0002925329,0.0002171951,0.0001073075,0.00004231763,0.0000512431,0.003003991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7004618,"threshold_uncertainty_score":0.5245479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3004779563647276,"score_gpt":0.4189119704325253,"score_spread":0.1184340140677977,"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."}}