{"id":"W4399706654","doi":"10.1016/j.jval.2024.03.2059","title":"PCR180 Measuring and Valuing Child Health in Canada","year":2024,"lang":"en","type":"article","venue":"Value in Health","topic":"Child and Adolescent Health","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of Toronto; McMaster University","funders":"","keywords":"Data science; Geography; Psychology; Computer science","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":[0.001323904,0.0001516894,0.0002349346,0.001632965,0.002843617,0.001808635,0.0009349831,0.0003899905,0.003470249],"category_scores_gemma":[0.006508548,0.0002322269,0.0003085474,0.003564438,0.0006109189,0.0003848944,0.0007555431,0.001178572,0.000260835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05942604,"about_ca_system_score_gemma":0.08903492,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9974405,"about_ca_topic_score_gemma":0.9988306,"domain_scores_codex":[0.9981641,0.0002846703,0.00006019995,0.00009480322,0.0008994358,0.0004968045],"domain_scores_gemma":[0.9938459,0.0007231592,0.000472259,0.00007558905,0.003377481,0.0015056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001157711,0.0002488944,0.9046406,0.0001073536,0.00004411481,0.0001554207,0.003659648,0.0007615355,0.0002294338,0.003403666,0.01805975,0.06857385],"study_design_scores_gemma":[0.00001596015,0.00005796339,0.9734493,0.00009845772,0.00002210155,0.00005293904,0.007413033,0.001594798,0.0003365273,0.0002560327,0.01668099,0.00002187967],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9363347,0.001280416,0.0007038574,0.005564082,0.00005821123,0.0001647406,0.007101898,0.00005035025,0.04874155],"genre_scores_gemma":[0.9857028,0.0008442116,0.001352861,0.0004319005,0.00001173285,0.0000492025,0.001142375,0.00001594762,0.01044888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05942604,"threshold_uncertainty_score":0.4311681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08862372594407637,"score_gpt":0.3659786101130777,"score_spread":0.2773548841690013,"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."}}