{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004795246,0.0001677396,0.0002062861,0.001037923,0.0003916417,0.0006415778,0.0003773344,0.0003986725,0.00224034],"category_scores_gemma":[0.002046416,0.0001667892,0.0001310989,0.000887402,0.00026369,0.0003158004,0.0005008906,0.0004990667,0.0004160302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004204389,"about_ca_system_score_gemma":0.0004563201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006906294,"about_ca_topic_score_gemma":0.01061993,"domain_scores_codex":[0.9995781,0.00009738095,0.00003003848,0.0001003757,0.0001263173,0.00006778935],"domain_scores_gemma":[0.998773,0.0003951591,0.0003425581,0.00008010279,0.000244497,0.0001645249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005608204,0.00005589691,0.9799911,0.00004754127,0.00003907253,0.0004400328,0.00019425,0.0002177942,0.004438297,0.00007675556,0.001145433,0.01279304],"study_design_scores_gemma":[0.00001839579,0.0004539294,0.9866287,0.00003765727,0.00005521273,0.002535652,0.0005722422,0.001536257,0.005205552,0.0001471413,0.002797722,0.00001167351],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961526,0.000528364,0.0004587797,0.0003237686,0.000005773406,0.00002162742,0.00102568,0.00003809453,0.001445187],"genre_scores_gemma":[0.9973787,0.0002528391,0.0006621464,0.0002430851,0.00001475573,0.00002588517,0.0009486958,0.00001322747,0.0004607092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006906294,"threshold_uncertainty_score":0.01373219,"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."}}