{"id":"W2099320460","doi":"10.1002/sim.6523","title":"Adaptive sampling in two‐phase designs: a biomarker study for progression in arthritis","year":2015,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Estimator; Computer science; Sampling (signal processing); Exploit; Adaptive sampling; Optimal design; Sample size determination; Phase (matter); Biomarker; Resource allocation; Statistics; Data mining; Machine learning; Mathematics; Monte Carlo method","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.04833581,0.001145704,0.001495747,0.0008237596,0.000621972,0.001085667,0.001725665,0.002220572,0.002201217],"category_scores_gemma":[0.1077635,0.0006418145,0.001704307,0.0007657084,0.002556804,0.001216612,0.001613477,0.001952842,0.0002307086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007510737,"about_ca_system_score_gemma":0.001775689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004506641,"about_ca_topic_score_gemma":0.0004626428,"domain_scores_codex":[0.944258,0.05155675,0.0005255619,0.001645819,0.001722844,0.0002910714],"domain_scores_gemma":[0.9181011,0.07045214,0.003732721,0.004854066,0.002176055,0.0006839235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01973063,0.003127371,0.01503332,0.002097697,0.001478202,0.000508571,0.00127102,0.2083595,0.01445681,0.4039982,0.002285495,0.3276532],"study_design_scores_gemma":[0.00652541,0.02579275,0.005794747,0.0003390126,0.0008386475,0.0002689436,0.0001730419,0.7286752,0.005841655,0.2184664,0.007048763,0.0002353848],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04723827,0.0005376926,0.9494162,0.0005154008,0.0001824949,0.00119225,0.00003879651,0.00008029085,0.0007985759],"genre_scores_gemma":[0.4477696,0.0004422649,0.5466694,0.0004085231,0.0001371501,0.003619292,0.00005923477,0.00002319397,0.0008713572],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04833581,"threshold_uncertainty_score":0.2556272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5332255921247048,"score_gpt":0.6223730082861888,"score_spread":0.08914741616148403,"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."}}