{"id":"W4367394417","doi":"10.1007/s12351-023-00774-w","title":"A multi-objective constrained partially observable Markov decision process model for breast cancer screening","year":2023,"lang":"en","type":"article","venue":"Operational Research","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mammography; Breast cancer; Breast cancer screening; Markov decision process; Medicine; Computer science; Actuarial science; Cancer; Markov process; Economics; Statistics; 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":[0.001716055,0.000124719,0.0002105571,0.0001948016,0.0005545624,0.0001144278,0.0001666467,0.00009703069,0.000289526],"category_scores_gemma":[0.0009897287,0.0001068705,0.00007738084,0.0008009254,0.0001305915,0.0003053479,0.00008075351,0.0002969888,0.00004395008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001883762,"about_ca_system_score_gemma":0.001999906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003323647,"about_ca_topic_score_gemma":0.0005840297,"domain_scores_codex":[0.9974036,0.00005182638,0.0002564871,0.0004354649,0.001270252,0.0005823635],"domain_scores_gemma":[0.996848,0.0004005964,0.0000278146,0.0001707889,0.002330102,0.000222647],"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":[0.01676667,0.0004682599,0.0883587,0.000514495,0.0004511469,0.0002029015,0.003335654,0.5828658,0.0608001,0.003587832,0.05209767,0.1905508],"study_design_scores_gemma":[0.001902926,0.0001236551,0.04787357,0.0003529523,0.00001459342,0.00002580958,0.0006830606,0.9475634,0.0007140469,0.0003902583,0.0002392882,0.0001165186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8060853,0.0003658039,0.1685287,0.01708917,0.0001724244,0.003998326,0.001482715,0.0002238807,0.002053608],"genre_scores_gemma":[0.9641014,0.00006538369,0.02930239,0.0004647178,0.0002599939,0.0009449568,0.0001355933,0.00002755071,0.004697956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3646975,"threshold_uncertainty_score":0.4358051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3394264910687215,"score_gpt":0.5076645604168483,"score_spread":0.1682380693481268,"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."}}