{"id":"W2885929747","doi":"10.1158/1538-7445.am2018-1033","title":"Abstract 1033: Patient-derived xenografts for prognostication and personalized treatment for head and neck squamous cell carcinoma","year":2018,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Medicine; Oncology; Head and neck squamous-cell carcinoma; Internal medicine; Radiation therapy; Hazard ratio; Cohort; Cancer; Head and neck cancer; Personalized medicine; Bioinformatics","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":[0.001221116,0.0002189557,0.0003624211,0.0002587834,0.0001632362,0.0005972226,0.000213621,0.0003004693,0.001889682],"category_scores_gemma":[0.0006362355,0.0001476189,0.0002486776,0.000334534,0.0001577404,0.0003181017,0.0002323707,0.0007481349,0.0005486677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000366318,"about_ca_system_score_gemma":0.000411948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008643467,"about_ca_topic_score_gemma":0.0007788,"domain_scores_codex":[0.999772,0.00007501962,0.00001859036,0.000028297,0.00007640475,0.00002968324],"domain_scores_gemma":[0.9996552,0.00009518356,0.0000554825,0.00006670057,0.00007429882,0.00005309016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004655366,0.001926699,0.05510267,0.0002720094,0.0001326218,0.0004004259,0.0002073262,0.003278406,0.8879939,0.0008725337,0.004980709,0.04017735],"study_design_scores_gemma":[0.0006104271,0.01807849,0.1840139,0.0001099865,0.0003528323,0.0038348,0.0004245645,0.03927497,0.7277381,0.0006400351,0.02486582,0.00005605965],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892061,0.001555906,0.004205019,0.000210083,0.00008872158,0.0002104887,0.002870997,0.00013467,0.001517989],"genre_scores_gemma":[0.9904658,0.0006463773,0.003783734,0.00007767916,0.00001401944,0.0001321891,0.003932008,0.0000186896,0.0009295375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001889682,"threshold_uncertainty_score":0.006457984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05693991319063952,"score_gpt":0.3657529792298571,"score_spread":0.3088130660392175,"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."}}