{"id":"W2025342001","doi":"10.1038/ejhg.2013.158","title":"Testing personalized medicine: patient and physician expectations of next-generation genomic sequencing in late-stage cancer care","year":2013,"lang":"en","type":"article","venue":"European Journal of Human Genetics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":120,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Institute for Cancer Research; Juravinski Cancer Centre; Princess Margaret Cancer Centre; University of Toronto; University Health Network; London Health Sciences Centre","funders":"Ontario Institute for Cancer Research","keywords":"Personalized medicine; Precision medicine; Context (archaeology); Thematic analysis; Medicine; Genetic testing; Cancer; Informed consent; Disease; Family medicine; Translational research; Qualitative research; Psychology; Alternative medicine; Bioinformatics; Internal medicine; Pathology","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":[0.0001031445,0.000108663,0.0001559178,0.00007467472,0.00006119435,0.00002513021,0.0001039157,0.00002171322,0.00001595913],"category_scores_gemma":[0.0000580766,0.0001025201,0.0000380021,0.00005826844,0.00008753774,0.00000624416,0.00005291323,0.00008172345,6.759177e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006114351,"about_ca_system_score_gemma":0.0001042282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000523551,"about_ca_topic_score_gemma":0.00006622112,"domain_scores_codex":[0.9991139,0.00009923901,0.0004310157,0.0001349626,0.0001015146,0.0001193816],"domain_scores_gemma":[0.9991524,0.00001699413,0.0003558702,0.0001175121,0.0002921665,0.00006505986],"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":[0.000005929799,0.000009389171,0.002764417,0.00002095112,0.00001908926,0.000007624983,0.004394817,0.009074868,0.9756464,0.000008996596,0.0001690168,0.007878521],"study_design_scores_gemma":[0.009998322,0.01036926,0.2046258,0.00118825,0.0004011705,0.00005592666,0.06555802,0.01094199,0.6847591,0.0001895339,0.01019176,0.001720866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98721,0.01187699,0.00007329186,0.00003257258,0.0001008927,0.0001096433,0.00001038393,0.000001348088,0.0005849064],"genre_scores_gemma":[0.9973264,0.0006819327,0.001372142,0.0001256366,0.0004272646,0.000002472956,0.00001601539,0.00002599179,0.00002216308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2908873,"threshold_uncertainty_score":0.4180648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04223194537394389,"score_gpt":0.2657779083322071,"score_spread":0.2235459629582632,"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."}}