{"id":"W2588409964","doi":"10.1007/s10985-017-9392-5","title":"Variable selection and prediction in biased samples with censored outcomes","year":2017,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Truncation (statistics); Event (particle physics); Psoriatic arthritis; Statistics; Econometrics; Computer science; Selection (genetic algorithm); Cohort; Selection bias; Interval (graph theory); Medicine; Mathematics; Machine learning; Arthritis; Internal medicine","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":[0.07992198,0.001178258,0.00308619,0.00221541,0.001307008,0.002609825,0.004889105,0.002712853,0.003348179],"category_scores_gemma":[0.2570499,0.001394167,0.001825294,0.002938659,0.00541607,0.004666538,0.003333627,0.004244591,0.0003923284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001598408,"about_ca_system_score_gemma":0.002312171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003677413,"about_ca_topic_score_gemma":0.002693689,"domain_scores_codex":[0.9721767,0.02290556,0.0006640992,0.001981843,0.001550221,0.0007217458],"domain_scores_gemma":[0.5895662,0.3840999,0.009494705,0.01259976,0.003055454,0.001184092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005655313,0.0001936086,0.03254725,0.0005928334,0.0007371145,0.0007981742,0.001361411,0.1407831,0.0003010339,0.7239513,0.007755334,0.09041325],"study_design_scores_gemma":[0.00008911904,0.00005337842,0.001737102,0.0001094417,0.00009146548,0.0001570289,0.00009533508,0.4072933,0.0002172239,0.5887462,0.001389065,0.00002135722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03750535,0.001716362,0.9573338,0.002096821,0.0001460236,0.00007665208,0.0001955516,0.0001626327,0.000766693],"genre_scores_gemma":[0.6659836,0.002673762,0.3206174,0.001261259,0.0009874831,0.001026195,0.001313962,0.0002068462,0.005929512],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07992198,"threshold_uncertainty_score":0.4226729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1228237240714166,"score_gpt":0.3878475692944712,"score_spread":0.2650238452230546,"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."}}