{"id":"W3131383445","doi":"10.1063/5.0022238","title":"Dynamical systems analysis as an additional tool to inform treatment outcomes: The case study of a quantitative systems pharmacology model of immuno-oncology","year":2021,"lang":"en","type":"article","venue":"Chaos An Interdisciplinary Journal of Nonlinear Science","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Pfizer","keywords":"Systems pharmacology; Systems biology; Tumor microenvironment; Immune system; Dynamical systems theory; Immunotherapy; Computational biology; Radiation therapy; Medicine; Cancer; Computer science; Oncology; Bioinformatics; Biology; Immunology; Drug; Pharmacology; Internal medicine; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.002149957,0.0002528937,0.001253027,0.0005952287,0.0003614495,0.00005317615,0.000870491,0.00008823191,0.0001806112],"category_scores_gemma":[0.0005225166,0.000146937,0.0002933657,0.001272594,0.0006674488,0.0004245998,0.0005820678,0.0002504482,0.000004419146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003767843,"about_ca_system_score_gemma":0.0008781347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004228129,"about_ca_topic_score_gemma":0.0001573553,"domain_scores_codex":[0.9964931,0.0004593107,0.00176806,0.0003425453,0.0006188184,0.0003181696],"domain_scores_gemma":[0.9948447,0.00150931,0.001257501,0.0005686969,0.001577337,0.0002424128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004745896,0.09532573,0.004141901,0.001349924,0.01936714,0.02445635,0.4345839,0.1970939,0.07329769,0.1423312,0.00081113,0.002495228],"study_design_scores_gemma":[0.00134568,0.01333544,0.0002413549,0.0001052632,0.001205933,0.007338868,0.1754278,0.7975308,0.00044797,0.002790051,0.000007254141,0.0002235949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935306,0.00002628286,0.00475105,0.000230881,0.0003319515,0.0005688019,0.0004213232,0.000009101743,0.0001299709],"genre_scores_gemma":[0.9879661,0.000002808791,0.01180345,0.00002302766,0.00006081852,0.00005070361,0.00001271855,0.00001293647,0.00006742262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6004369,"threshold_uncertainty_score":0.5991918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09654655479824328,"score_gpt":0.4611945039083278,"score_spread":0.3646479491100845,"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."}}