{"id":"W2395228578","doi":"10.1186/s41199-016-0004-y","title":"Genomically personalized therapy in head and neck cancer","year":2016,"lang":"en","type":"review","venue":"Cancers of the Head & Neck","topic":"Head and Neck Cancer Studies","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network","funders":"","keywords":"Head and neck squamous-cell carcinoma; Head and neck cancer; Precision medicine; Personalized medicine; Malignancy; Clinical trial; Genomics; Oncology; Medicine; Cancer; Biomarker; Biomarker discovery; Bioinformatics; Internal medicine; Computational biology; Genome; Pathology; Biology; Proteomics; Gene","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":[0.0007079351,0.0005067477,0.000974956,0.001749267,0.0002345179,0.0008742515,0.0006267324,0.0009215316,0.003249699],"category_scores_gemma":[0.001062962,0.0001600323,0.0005649819,0.001582283,0.0004507131,0.001049974,0.0006922765,0.001282373,0.001087639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006643647,"about_ca_system_score_gemma":0.00111462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001254383,"about_ca_topic_score_gemma":0.002464539,"domain_scores_codex":[0.999801,0.00006281763,0.00002948452,0.00003086299,0.00006143754,0.0000145808],"domain_scores_gemma":[0.9996634,0.0002140598,0.0000350352,0.0000124057,0.0000594891,0.00001568408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004433053,0.00004483768,0.000189374,0.02054265,0.0001453689,0.0001484743,0.00007222444,0.0003368977,0.0006548894,0.005650949,0.02172593,0.9504442],"study_design_scores_gemma":[0.00001534805,0.00006526124,0.0009206396,0.009538827,0.0001891212,0.001108843,0.00009277979,0.00008302675,0.0003374628,0.003606344,0.9840257,0.00001670859],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006587019,0.998868,0.00006648806,0.0003091839,0.00009729229,0.000002757134,0.00001053544,0.000002846104,0.0005771132],"genre_scores_gemma":[0.000722673,0.9986547,0.0001075234,0.0001916935,0.00008451603,0.000003912386,0.00001712895,7.74152e-7,0.0002170101],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003249699,"threshold_uncertainty_score":0.01087135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07357988163979376,"score_gpt":0.3827926424840124,"score_spread":0.3092127608442187,"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."}}