{"id":"W3010894009","doi":"10.1038/s41598-020-62023-w","title":"Machine Learning and Feature Selection Applied to SEER Data to Reliably Assess Thyroid Cancer Prognosis","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Thyroid Cancer Diagnosis and Treatment","field":"Medicine","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; Université de Montréal","funders":"","keywords":"Machine learning; Artificial intelligence; Feature selection; Linear discriminant analysis; Computer science; Perceptron; Medical diagnosis; Thyroid cancer; Clinical trial; Feature (linguistics); Medicine; Artificial neural network; Cancer; Internal medicine; Pathology","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":[0.003517895,0.0006149269,0.0006534658,0.002610516,0.0002781611,0.0007614488,0.0004205297,0.0004833369,0.0007513833],"category_scores_gemma":[0.01237336,0.0001388861,0.0005459891,0.001883739,0.0003309579,0.0005120624,0.000453536,0.0006368609,0.0003582028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005614578,"about_ca_system_score_gemma":0.0005516378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002743088,"about_ca_topic_score_gemma":0.002947244,"domain_scores_codex":[0.9980861,0.001043033,0.0001735803,0.0002476388,0.0003363552,0.0001133538],"domain_scores_gemma":[0.9947076,0.003539845,0.0004759906,0.0006479,0.000518506,0.0001101728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001242249,0.0006186669,0.4353996,0.0002088449,0.0005813468,0.0006376925,0.0002737429,0.1260179,0.01802914,0.001599472,0.005400307,0.4099911],"study_design_scores_gemma":[0.00005661486,0.0005900394,0.2330034,0.00004718436,0.0001015098,0.0006458592,0.0002208815,0.7480921,0.01089988,0.003504644,0.002776734,0.00006120207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8977575,0.0008378655,0.09350833,0.000762061,0.0001047414,0.0002121239,0.003960243,0.0009468133,0.001910281],"genre_scores_gemma":[0.9636456,0.0001152824,0.03316317,0.00005481028,0.00004980562,0.00008125503,0.002609709,0.00001922364,0.0002610731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003517895,"threshold_uncertainty_score":0.01860464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04473174573780529,"score_gpt":0.314095128125355,"score_spread":0.2693633823875497,"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."}}