{"id":"W4408651332","doi":"10.1002/sim.70023","title":"Variable Selection for Progressive Multistate Processes Under Intermittent Observation","year":2025,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Maximization; Variable (mathematics); Selection (genetic algorithm); Feature selection; Expectation–maximization algorithm; Regression; Exploit; Poisson regression; Machine learning; Artificial intelligence; Statistics; Mathematical optimization; Maximum likelihood; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006190648,0.0001466219,0.0003235909,0.0001316126,0.0000770016,0.00001837055,0.0001108091,0.00006651506,0.0001234739],"category_scores_gemma":[0.01942258,0.0001175002,0.000009984657,0.0004978154,0.0001294731,0.00004180896,0.00002580801,0.0001581607,0.000001217921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001089213,"about_ca_system_score_gemma":0.0001748852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008144375,"about_ca_topic_score_gemma":0.0001088253,"domain_scores_codex":[0.9987379,0.00008098815,0.0005065513,0.0002568429,0.0001789876,0.0002386909],"domain_scores_gemma":[0.9945552,0.004520211,0.0001571305,0.0001259049,0.0005987742,0.00004274384],"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":[0.00006756542,0.00008804062,0.0007338029,0.001236501,0.00002664646,0.000002112245,0.0002454249,0.00001595982,0.0001607985,0.959688,0.01050146,0.02723367],"study_design_scores_gemma":[0.001052916,0.0002262799,0.002385024,0.0008477488,0.00007143962,0.0000017262,0.0002683558,0.02518359,0.0001786765,0.9686722,0.001001193,0.0001108073],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006742021,0.0000895554,0.9962367,0.0005209383,0.000476092,0.0007121806,0.0001268489,0.0000432959,0.001120192],"genre_scores_gemma":[0.01120583,0.00001914091,0.9861292,0.0004198827,0.00008633533,0.000295976,0.00005925144,0.00001632874,0.00176809],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02712286,"threshold_uncertainty_score":0.9888372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1040795329278424,"score_gpt":0.4503327739983338,"score_spread":0.3462532410704914,"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."}}