{"id":"W2951672714","doi":"10.1101/469957","title":"Proliferation Saturation Index in an adaptive Bayesian approach to predict patient-specific radiotherapy responses","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre","funders":"National Cancer Institute; National Institutes of Health; Moffitt Cancer Center","keywords":"Radiation therapy; Bayesian probability; Imaging biomarker; Computer science; Index (typography); Biomarker; Medicine; Statistics; Nuclear medicine; Radiology; Mathematics; Artificial intelligence; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002025864,0.0005324497,0.0006462131,0.000999826,0.0002386812,0.0007474761,0.0008579077,0.0009005741,0.00108201],"category_scores_gemma":[0.006334994,0.0006187786,0.0006518833,0.0004671677,0.0004922304,0.0005927588,0.0005565218,0.000747723,0.0002493944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001329413,"about_ca_system_score_gemma":0.001142436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009980102,"about_ca_topic_score_gemma":0.007015299,"domain_scores_codex":[0.9994981,0.0002441599,0.00002145653,0.00009833849,0.00009593341,0.00004204832],"domain_scores_gemma":[0.9978661,0.001531841,0.0002669518,0.00007820561,0.0001835192,0.0000734063],"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":[0.00007348756,0.00004916366,0.003960357,0.00001865919,0.00003822587,0.00001895737,0.00002542442,0.9797217,0.001742883,0.001258108,0.0001584381,0.01293479],"study_design_scores_gemma":[0.000003581488,0.00001758342,0.0005182221,0.000002769629,0.000005882203,0.000005872903,0.000002408119,0.9982867,0.0003247049,0.0007659696,0.00006116444,0.000005088666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2827522,0.0004212701,0.7124885,0.0007095351,0.00002779898,0.0001436406,0.0003237969,0.0005118198,0.002621371],"genre_scores_gemma":[0.9608546,0.0001472503,0.03724927,0.0001129711,0.00002282417,0.0001682548,0.0001829656,0.00003350951,0.001228377],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009980102,"threshold_uncertainty_score":0.01984406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01382577180358367,"score_gpt":0.2438454115366529,"score_spread":0.2300196397330692,"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."}}