{"id":"W2149190289","doi":"10.1002/hec.1244","title":"An opportunity cost approach to sample size calculation in cost‐effectiveness analysis","year":2007,"lang":"en","type":"article","venue":"Health Economics","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Sample size determination; Statistics; Sample (material); Econometrics; Cost analysis; Computer science; Mathematics; Operations research; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.04746499,0.0003182175,0.00185864,0.001295906,0.0002910053,0.0001164759,0.0003854504,0.0002573054,0.0001807443],"category_scores_gemma":[0.00403926,0.0004698362,0.0002000293,0.0009769176,0.00005380419,0.0006838432,0.00005672283,0.0003006601,0.0003323968],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004232655,"about_ca_system_score_gemma":0.0006227391,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01734408,"about_ca_topic_score_gemma":0.01117858,"domain_scores_codex":[0.9917541,0.000684274,0.005329738,0.001133196,0.00007199003,0.001026763],"domain_scores_gemma":[0.9919279,0.003612135,0.002110505,0.001000545,0.00007445851,0.001274436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002341091,0.0008832519,0.7708015,0.0005912452,0.0002537092,0.000001132399,0.003981918,0.107692,8.388307e-7,0.1086924,0.00115247,0.005715434],"study_design_scores_gemma":[0.001208848,0.0001736998,0.8733115,0.00002624023,0.00001637823,0.00000276509,0.0009212774,0.09033478,0.000002823093,0.004941877,0.02846668,0.0005931191],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7614832,0.0002407279,0.2212165,0.006356478,0.0005969886,0.00421051,0.00115391,0.000104986,0.004636735],"genre_scores_gemma":[0.9606209,0.0001143051,0.01763357,0.02044745,0.0002734915,0.000346888,0.0004486386,0.00006177437,0.00005292879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2035829,"threshold_uncertainty_score":0.9997754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4752125649548686,"score_gpt":0.4894653035946742,"score_spread":0.01425273863980558,"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."}}