{"id":"W2083696559","doi":"10.1016/j.ijnurstu.2015.02.006","title":"Conducting a two-stage preference trial: Utility and challenges","year":2015,"lang":"en","type":"article","venue":"International Journal of Nursing Studies","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; Toronto Metropolitan University","funders":"National Institute of Nursing Research; Canadian Institutes of Health Research","keywords":"Preference; Selection (genetic algorithm); Protocol (science); Stage (stratigraphy); Intervention (counseling); Randomized controlled trial; Psychology; Clinical trial; Process (computing); Medicine; Alternative medicine; Computer science; Nursing; Artificial intelligence; Microeconomics; Economics; Surgery","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":[],"consensus_categories":[],"category_scores_codex":[0.0009442843,0.00006976368,0.0002290468,0.00009875665,0.00003255568,0.00003049986,0.0001172491,0.0000221657,0.00001986941],"category_scores_gemma":[0.000330366,0.00007013611,0.00005323761,0.00001635661,0.0001046631,0.0003157681,0.00003409131,0.00008129152,0.00001377518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002273447,"about_ca_system_score_gemma":0.00001194355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001011255,"about_ca_topic_score_gemma":0.000003069154,"domain_scores_codex":[0.9992511,0.00002012036,0.0004707769,0.0001218711,0.00005958939,0.00007651677],"domain_scores_gemma":[0.9992296,0.00006243931,0.0005224639,0.00005516953,0.00008561427,0.00004476369],"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":[0.00838527,0.001833455,0.2284863,0.00008415801,0.004762297,0.00005549492,0.09682391,0.001143626,0.00008398511,0.3306361,0.004612133,0.3230932],"study_design_scores_gemma":[0.09338439,0.002286107,0.2186994,0.001004601,0.0001475643,0.0003630673,0.09147348,0.002829631,0.0004203515,0.5500445,0.03830775,0.001039146],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9502516,0.03660487,0.0002447232,0.004034637,0.002280102,0.00006565057,0.00000973385,0.000005364505,0.006503366],"genre_scores_gemma":[0.9940903,0.004731152,0.0007058909,0.00004811259,0.0002762318,0.000001917183,7.420093e-7,0.000005447579,0.0001402239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.322054,"threshold_uncertainty_score":0.2860067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8340923462592781,"score_gpt":0.4051556381102072,"score_spread":0.4289367081490709,"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."}}