{"id":"W3028101882","doi":"10.1016/j.jval.2020.04.1748","title":"PCN282 VALIDATION AND CALIBRATION OF A DISCRETE EVENT SIMULATION TREATMENT SEQUENCING MODEL TO ESTIMATE LONG-TERM SURVIVAL OF PATIENTS WITH MULTIPLE MYELOMA","year":2020,"lang":"en","type":"article","venue":"Value in Health","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"EVERSANA (Canada)","funders":"","keywords":"Medicine; Survival analysis; Proportional hazards model; Calibration; Population; Event (particle physics); Cancer registry; Overall survival; Oncology; Multiple myeloma; Progression-free survival; Term (time); Covariate; Internal medicine; Statistics; Cancer; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0008965019,0.000132986,0.0005350252,0.00005750725,0.00002910475,0.000008464015,0.00005553708,0.0000558494,0.000004426334],"category_scores_gemma":[0.008268855,0.0001055738,0.00003355186,0.0001840239,0.00003301176,0.00007954814,0.00003382599,0.0000618937,2.658655e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002196624,"about_ca_system_score_gemma":0.0001668809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001812669,"about_ca_topic_score_gemma":0.00002802059,"domain_scores_codex":[0.9976242,0.0006057973,0.001022196,0.0002680397,0.0003175665,0.0001622077],"domain_scores_gemma":[0.992717,0.006394168,0.0004739809,0.0001714308,0.00008788419,0.000155558],"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":[0.0005414411,0.0001198563,0.1441916,0.0008149936,0.0000209885,6.104588e-7,0.001849081,0.846266,0.0001083745,0.002365593,5.719046e-7,0.003720877],"study_design_scores_gemma":[0.001666776,0.001143192,0.03681279,0.0003072885,0.00002833503,6.523706e-8,0.00001999511,0.9393747,0.001143248,0.01941054,1.048112e-7,0.00009294297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5433307,0.000003849866,0.4556778,0.0001294179,0.00002460504,0.0007312176,0.0000889599,0.00001147116,0.000002001952],"genre_scores_gemma":[0.6716194,0.000003697617,0.3282967,0.00002254387,0.00001506006,0.00001497707,0.00001189204,0.00001470124,0.000001059021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1282887,"threshold_uncertainty_score":0.9899188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5010529218027832,"score_gpt":0.5316223444785074,"score_spread":0.03056942267572427,"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."}}