{"id":"W4313426611","doi":"10.1101/2022.12.27.522039","title":"A Clinically Useful and Biologically Informative Genomic Classifier for Papillary Thyroid Cancer","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Thyroid Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; University of Calgary","funders":"","keywords":"Risk stratification; Thyroid cancer; Classifier (UML); Papillary thyroid cancer; Computational biology; Oncology; Internal medicine; Thyroid; Medicine; Bioinformatics; Biology; Artificial intelligence; Computer science","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":[0.0004197233,0.0003266462,0.0003465697,0.0009699266,0.0002550796,0.0007441761,0.0002270263,0.0004132444,0.0009996288],"category_scores_gemma":[0.00183294,0.00009292485,0.0002767699,0.0004641,0.0001716768,0.00017406,0.0002623501,0.0004005011,0.0004762477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000517113,"about_ca_system_score_gemma":0.0007170441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002252368,"about_ca_topic_score_gemma":0.002362182,"domain_scores_codex":[0.9997817,0.00004289325,0.00001849725,0.00006322161,0.00006281979,0.00003091161],"domain_scores_gemma":[0.999548,0.0002428532,0.00004629827,0.00003366065,0.000105616,0.00002350472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005928585,0.000238267,0.296605,0.0001950528,0.0001546647,0.0006578148,0.00009179321,0.05083987,0.08150961,0.003047948,0.01656612,0.549501],"study_design_scores_gemma":[0.0001160135,0.0003390319,0.1595568,0.00007291531,0.0003245219,0.002106935,0.0002027008,0.7302263,0.07180707,0.01909725,0.01608368,0.00006670029],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8088253,0.001332495,0.1734471,0.00154238,0.0001321709,0.0001874487,0.008487084,0.002306409,0.00373959],"genre_scores_gemma":[0.9195932,0.0001711817,0.07308947,0.0001362014,0.00006621824,0.00008398821,0.005916716,0.00005027654,0.0008927637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002252368,"threshold_uncertainty_score":0.004478455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03111872400820255,"score_gpt":0.2833683203979439,"score_spread":0.2522495963897414,"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."}}