{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01681547,0.0002189865,0.0006904062,0.000936709,0.00004038219,0.00004562034,0.0004157151,0.00005715426,0.00009064612],"category_scores_gemma":[0.02275156,0.0001645401,0.00002063214,0.001445801,0.0002559694,0.0001788721,0.0001139775,0.0002351264,0.00001343715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002439845,"about_ca_system_score_gemma":0.0001673373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004204844,"about_ca_topic_score_gemma":0.001370751,"domain_scores_codex":[0.9950075,0.001178837,0.00135897,0.0006596996,0.001375219,0.0004197959],"domain_scores_gemma":[0.9933094,0.005585012,0.0002466503,0.0003700105,0.0002906165,0.0001983057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003311131,0.002182264,0.05219485,0.00001034054,0.00001402821,0.0007217414,0.01972146,0.0004948592,0.002937945,0.004077883,0.003632787,0.9107007],"study_design_scores_gemma":[0.08519574,0.01592045,0.04915426,0.001959624,0.00002886716,0.00003102818,0.1176389,0.2505443,0.000721098,0.4770866,0.0008112194,0.0009079779],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1043509,0.001606732,0.8900298,0.0001457448,0.0005935245,0.002568183,0.00008724842,0.00001852951,0.0005993692],"genre_scores_gemma":[0.5425953,0.00001357011,0.4570244,0.00005603696,0.00004182171,0.0002119647,0.000009784405,0.00001515714,0.00003196095],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9097927,"threshold_uncertainty_score":0.9854802,"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."}}