{"id":"W4388674303","doi":"10.3233/cbm-230181","title":"Subgroup identification of targeted therapy effects on biomarker for time to event data","year":2023,"lang":"en","type":"article","venue":"Cancer Biomarkers","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua","funders":"","keywords":"Biomarker; Oncology; Medicine; Event (particle physics); Identification (biology); Markov chain Monte Carlo; non-small cell lung cancer (NSCLC); Hazard ratio; Internal medicine; Lung cancer; Bayesian probability; Computer science; Artificial intelligence; Confidence interval","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00455658,0.0001912486,0.0004854676,0.0001839963,0.00005957136,0.0000230453,0.0006527396,0.0001317147,0.0002984767],"category_scores_gemma":[0.03301223,0.0001617795,0.0001319329,0.0007867148,0.00008047685,0.00005193105,0.0001385484,0.00005947839,0.0002072282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007723117,"about_ca_system_score_gemma":0.00006475079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001181132,"about_ca_topic_score_gemma":0.000001861577,"domain_scores_codex":[0.9974433,0.0004372271,0.0008408157,0.0006012225,0.0003623604,0.0003150853],"domain_scores_gemma":[0.9723607,0.02587591,0.000326427,0.001173091,0.0001308139,0.0001330743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002022723,0.0001709102,0.0001533011,0.0005097761,0.0007178974,0.000003078753,0.00007764276,0.000004295197,0.2255289,0.001335066,0.3206734,0.448803],"study_design_scores_gemma":[0.007763151,0.001229653,0.05881413,0.001009436,0.0004680688,8.713869e-7,0.00006222923,0.01684005,0.4455303,0.4408037,0.02631361,0.001164797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4971622,0.001009524,0.4327637,0.01370959,0.01169452,0.02190226,0.01916933,0.001520558,0.001068287],"genre_scores_gemma":[0.2861203,0.0007027681,0.6963574,0.002282325,0.001738423,0.005150012,0.001028449,0.0005962379,0.00602412],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4476382,"threshold_uncertainty_score":0.9751331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4912394440649165,"score_gpt":0.5702434041205268,"score_spread":0.07900396005561028,"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."}}