{"id":"W3035309353","doi":"10.1158/1557-3265.aacrahns19-ia02","title":"Abstract IA02: Utilizing patient-derived xenografts for prognostication and biomarker discovery","year":2020,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Lung Cancer Research Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Princess Margaret Cancer Centre","funders":"","keywords":"Medicine; Oncology; Head and neck squamous-cell carcinoma; Internal medicine; Biomarker; Hazard ratio; Radiation therapy; Cohort; Cancer; Precision medicine; Personalized medicine; Bioinformatics; Head and neck cancer; Pathology; Biology; Confidence interval","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":[0.002214563,0.0003951476,0.0004082748,0.0004356574,0.0001918453,0.001255553,0.0003962342,0.0004252244,0.001435802],"category_scores_gemma":[0.001097763,0.0002125535,0.0002971349,0.0004620012,0.0002719606,0.0004371885,0.000493537,0.001018358,0.0006128614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003525616,"about_ca_system_score_gemma":0.0003884719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007026487,"about_ca_topic_score_gemma":0.0005633606,"domain_scores_codex":[0.9995334,0.0001814277,0.00003872849,0.00008851413,0.0001044302,0.00005357754],"domain_scores_gemma":[0.9992588,0.0001827951,0.0001115013,0.0002071174,0.0001359718,0.0001038686],"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":[0.00405479,0.001911331,0.08199766,0.0002666521,0.0002047671,0.0003494436,0.0001876413,0.003858282,0.851949,0.001355273,0.003927935,0.0499373],"study_design_scores_gemma":[0.0005149729,0.01721546,0.114289,0.00008745556,0.0004139822,0.003992306,0.0004250024,0.045166,0.7928246,0.001029804,0.02396721,0.00007414913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799356,0.001767386,0.01183456,0.0002783925,0.0001104115,0.0002956822,0.002993946,0.000308585,0.002475503],"genre_scores_gemma":[0.9835266,0.0007953041,0.007980146,0.0001342197,0.00002521058,0.0001878778,0.006087509,0.00003164408,0.001231381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002214563,"threshold_uncertainty_score":0.0117119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4849151068034955,"score_gpt":0.5792740164811869,"score_spread":0.09435890967769145,"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."}}