{"id":"W4280543579","doi":"10.1002/sim.9433","title":"Dynamic prediction with time‐dependent marker in survival analysis using supervised functional principal component analysis","year":2022,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Division of Cancer Prevention, National Cancer Institute","keywords":"Principal component analysis; Computer science; Functional principal component analysis; Event (particle physics); Artificial intelligence; Independent component analysis; Data mining; Machine learning; Pattern recognition (psychology); Feature (linguistics)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002523718,0.0002207641,0.0008425383,0.001129056,0.0001365399,0.00001447909,0.0001648193,0.00004693057,0.006651385],"category_scores_gemma":[0.0009364889,0.0001857636,0.00005942222,0.002825333,0.0001582744,0.00003534009,0.0001174028,0.0004714244,0.000002203369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005221286,"about_ca_system_score_gemma":0.00009195897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006751005,"about_ca_topic_score_gemma":0.001263688,"domain_scores_codex":[0.9964273,0.0007787248,0.0008080161,0.0004569366,0.001196293,0.0003327305],"domain_scores_gemma":[0.9969829,0.00223039,0.0002028155,0.0003455016,0.0001295872,0.0001088484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001783065,0.001641537,0.6276764,0.0002896485,0.00678117,0.0007830635,0.003181584,0.2487971,0.001172878,0.1029019,0.0004034686,0.00458808],"study_design_scores_gemma":[0.001112224,0.0001863278,0.2601701,0.00001963558,0.002187987,0.000005352667,0.0006196834,0.7187362,9.866041e-7,0.01680268,0.00001067948,0.0001480789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1920435,0.0000232555,0.806112,0.00006517516,0.0001571145,0.0002290384,0.0009787377,0.00002186842,0.0003693386],"genre_scores_gemma":[0.7106103,0.000009688471,0.288368,0.0000450865,0.0000308215,0.00005308771,0.0006379688,0.00002283068,0.0002222249],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5185668,"threshold_uncertainty_score":0.9942567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05897852154404278,"score_gpt":0.3553308688059422,"score_spread":0.2963523472618994,"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."}}