{"id":"W2538407824","doi":"10.1186/s13073-016-0364-2","title":"Molecular profiling of advanced solid tumors and patient outcomes with genotype-matched clinical trials: the Princess Margaret IMPACT/COMPACT trial","year":2016,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":285,"is_retracted":false,"has_abstract":true,"ca_institutions":"Markham Stouffville Hospital; McMaster University; Grand River Hospital; University Health Network; University of Toronto; Hamilton Health Sciences; Princess Margaret Cancer Centre","funders":"University of Toronto; Ontario Ministry of Health and Long-Term Care; Princess Margaret Cancer Foundation; Cancer Care Ontario","keywords":"Profiling (computer programming); Genotype; Medicine; Clinical trial; Human genetics; Bioinformatics; Computational biology; Internal medicine; Oncology; Medical physics; Biology; Genetics; Computer science; Gene","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":[0.001430686,0.0002261625,0.0007764,0.00004181567,0.00004946071,0.000008850588,0.0002084754,0.00009204874,0.00002513775],"category_scores_gemma":[0.001848259,0.00009659638,0.000146614,0.00007506074,0.0003574463,0.00000359453,0.00009472061,0.00009324858,0.000001121049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002457812,"about_ca_system_score_gemma":0.0002045831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003584448,"about_ca_topic_score_gemma":0.00001525839,"domain_scores_codex":[0.9980268,0.0002504159,0.0008690567,0.0003651834,0.0002215951,0.0002669183],"domain_scores_gemma":[0.9982587,0.0004106515,0.0005258219,0.0005088172,0.0001342377,0.000161813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.03052232,0.0002376182,0.04063478,0.00005517867,0.0008906072,0.00002944467,0.0003929868,0.0004477033,0.8860438,0.0001909256,0.0003645411,0.04019003],"study_design_scores_gemma":[0.2425734,0.05420457,0.2478498,0.0007166298,0.001333172,0.000123909,0.002486653,0.00009085927,0.4147095,0.001394112,0.03246974,0.002047631],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926916,0.002761449,0.001369204,0.00178845,0.0002364123,0.0009729393,0.00006280009,0.000006967642,0.0001101253],"genre_scores_gemma":[0.9981542,0.0007494646,0.0002894484,0.0004155932,0.0002804867,0.00002886672,0.00003174311,0.00002816236,0.00002205282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4713344,"threshold_uncertainty_score":0.3939086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03158108628493941,"score_gpt":0.3682015252279748,"score_spread":0.3366204389430354,"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."}}