{"id":"W6998584216","doi":"","title":"Adjusting Historical Costs for Inflation with the Use of Standardized Automated Tools","year":2024,"lang":"fr","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inflation (cosmology); Data collection; Social security; Cost–benefit analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02273125,0.001040619,0.0009712124,0.005661406,0.0006038814,0.003660264,0.00182065,0.0007120913,0.0055398],"category_scores_gemma":[0.1884877,0.0006773166,0.001481117,0.008274094,0.0004265094,0.002660556,0.002315284,0.001539224,0.001227065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003563347,"about_ca_system_score_gemma":0.004770138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03123496,"about_ca_topic_score_gemma":0.0240705,"domain_scores_codex":[0.9694003,0.01825668,0.003388375,0.002091905,0.006037678,0.0008250552],"domain_scores_gemma":[0.8997669,0.05090445,0.0185567,0.01338798,0.01641025,0.0009736869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001399889,0.0004744,0.4791034,0.0006375497,0.001470039,0.0001504594,0.0005496329,0.0473385,0.0001972282,0.0155165,0.04756872,0.4055937],"study_design_scores_gemma":[0.0005848185,0.001238325,0.6388494,0.001539544,0.0008329981,0.0005823167,0.001062477,0.2557352,0.00210448,0.0335514,0.063602,0.0003169422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5082595,0.007846567,0.3533481,0.01236949,0.002377246,0.004626303,0.05142941,0.003214567,0.05652888],"genre_scores_gemma":[0.8632018,0.001510806,0.1137151,0.0006206581,0.0006540211,0.001733589,0.01531976,0.0004215811,0.002822721],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03123496,"threshold_uncertainty_score":0.1202158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5429584592677149,"score_gpt":0.6372045807010218,"score_spread":0.0942461214333069,"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."}}