{"id":"W4404580874","doi":"10.1016/j.jlb.2024.100224","title":"PAN-CANCER ANALYSIS OF PRE-TREATMENT CIRCULATING TUMOR DNA (CTDNA) IN PATIENTS FROM THE PRINCESS MARGARET LIQUID BIOPSY PROGRAM","year":2024,"lang":"en","type":"article","venue":"The Journal of Liquid Biopsy","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston Health Sciences Centre; Princess Margaret Cancer Centre","funders":"","keywords":"Liquid biopsy; Circulating tumor DNA; Cancer; DNA; Medicine; Internal medicine; Oncology; Biopsy; Biology; Genetics","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":[0.001136682,0.0002166241,0.0006398272,0.000289764,0.00007609418,0.00005051666,0.0003471354,0.00006232689,0.0001051124],"category_scores_gemma":[0.0002434286,0.0001022219,0.0003602966,0.001068064,0.0001863829,0.00009558287,0.00007781642,0.0005491229,0.000001979231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002137559,"about_ca_system_score_gemma":0.000215789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000780686,"about_ca_topic_score_gemma":0.00002476135,"domain_scores_codex":[0.9977663,0.0002489411,0.0008591423,0.0002022752,0.0006364553,0.0002868717],"domain_scores_gemma":[0.9984432,0.0004050476,0.0005235372,0.0003420361,0.0001660191,0.0001200878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009647531,0.002666106,0.7638312,0.0003910899,0.01433366,0.0007574082,0.01647501,0.003257846,0.07129547,0.0001082798,0.0003637541,0.1168726],"study_design_scores_gemma":[0.002383404,0.003955653,0.9473311,0.002441715,0.006953211,0.0001803655,0.00069323,0.02994707,0.003807678,0.00004592205,0.001990171,0.000270479],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919472,0.002478415,0.0001863334,0.004458888,0.0003795718,0.0004153384,0.00002552238,0.00002740186,0.00008128581],"genre_scores_gemma":[0.9981895,0.0003649179,0.0007138559,0.0002244186,0.000403691,0.00001476428,0.0000203915,0.00002963807,0.00003878991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1834999,"threshold_uncertainty_score":0.4168488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378782108264151,"score_gpt":0.3197902269119754,"score_spread":0.3060024058293339,"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."}}