{"id":"W4410783369","doi":"10.1002/sim.70121","title":"Integrating Complex Selection Rules Into the Latent Overlapping Group Lasso for the Construction of Coherent Prediction Models","year":2025,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Réseau Québécois de Recherche sur les Médicaments; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Heart and Stroke Foundation of Canada","keywords":"Interpretability; Feature selection; Lasso (programming language); Latent variable; Computer science; Variable (mathematics); Machine learning; Predictive modelling; Selection (genetic algorithm); Elastic net regularization; Proxy (statistics); Artificial intelligence; Data mining; Mathematics","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":[0.02840888,0.001813516,0.001874896,0.001538421,0.0008728519,0.002825164,0.001969622,0.001631641,0.002782407],"category_scores_gemma":[0.05830139,0.000944931,0.002067281,0.001544004,0.002223598,0.00257873,0.00372193,0.006071899,0.001063343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008441487,"about_ca_system_score_gemma":0.002678976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001598025,"about_ca_topic_score_gemma":0.00237095,"domain_scores_codex":[0.981237,0.01242062,0.001343914,0.001725572,0.00289438,0.0003785331],"domain_scores_gemma":[0.959426,0.03234455,0.003263062,0.002190885,0.00233507,0.0004404304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001447286,0.0001620006,0.004040856,0.0003284579,0.0002633335,0.0004804916,0.0005556174,0.6704191,0.003764203,0.2017985,0.004628506,0.1134142],"study_design_scores_gemma":[0.00002661017,0.00004122131,0.0001885403,0.00004879547,0.0000213246,0.00003788586,0.00002238067,0.921975,0.0007858906,0.0752617,0.00156949,0.00002106545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001246164,0.00003848452,0.9982165,0.000124009,0.00001048152,0.00004814893,0.00004392539,0.0000803252,0.000191979],"genre_scores_gemma":[0.07067007,0.0002117392,0.9262444,0.0003349451,0.0001172339,0.0009108307,0.0005352708,0.0002221351,0.0007532532],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02840888,"threshold_uncertainty_score":0.1502423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3549779274483894,"score_gpt":0.5195250836602353,"score_spread":0.1645471562118459,"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."}}