{"id":"W2961162542","doi":"10.1002/sim.8316","title":"Bayesian consensus‐based sample size criteria for binomial proportions","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Prior probability; Frequentist inference; Sample size determination; Bayesian probability; Statistics; Econometrics; Credible interval; Sample (material); Mathematics; Point estimation; Bayes' theorem; Computer science; Bayesian inference","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003995045,0.0001769538,0.000523061,0.0002581167,0.00007876287,0.00005126343,0.000388086,0.00007896664,0.02383039],"category_scores_gemma":[0.06313896,0.0001317533,0.0000441126,0.0005105844,0.0003734534,0.00004836914,0.00004619212,0.0001460206,0.0001136383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008701567,"about_ca_system_score_gemma":0.0002111516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001305307,"about_ca_topic_score_gemma":0.00004888759,"domain_scores_codex":[0.9969015,0.0003617923,0.000980063,0.0005252103,0.0008934852,0.000338025],"domain_scores_gemma":[0.9662405,0.03249185,0.0002536614,0.0005356783,0.0003286457,0.0001496912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001219052,0.0003628813,0.0161245,0.0001206096,0.00003488225,0.00007744051,0.001619958,0.0004043572,0.06679549,0.06008746,0.8161659,0.03698748],"study_design_scores_gemma":[0.008298786,0.002755231,0.01370385,0.0002069042,0.00005068413,0.00001483196,0.003149717,0.3117243,0.003127097,0.5702257,0.08605554,0.000687376],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004378801,0.00003273658,0.9843531,0.001734798,0.001534921,0.001171928,0.003332485,0.00003201393,0.00342924],"genre_scores_gemma":[0.1712205,0.000001141304,0.8264728,0.0006797071,0.0001429389,0.00007188835,0.00009035887,0.00002108202,0.001299608],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7301103,"threshold_uncertainty_score":0.977062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1486759724768557,"score_gpt":0.5096998485410166,"score_spread":0.3610238760641609,"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."}}