{"id":"W4413794335","doi":"10.3390/app15179397","title":"Machine Learning in Differentiated Thyroid Cancer Recurrence and Risk Prediction","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Thyroid Cancer Diagnosis and Treatment","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Health Authority; St. Francis Xavier University; University of Toronto","funders":"Nova Scotia Health Authority; Natural Sciences and Engineering Research Council of Canada; Compute Canada; Canada Foundation for Innovation; Nova Scotia Research Innovation Trust","keywords":"Feature selection; Machine learning; Artificial intelligence; Computer science; Random forest; Logistic regression; Gradient boosting","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00552246,0.0005981945,0.0008016228,0.001543993,0.0002981545,0.0009108046,0.0005777844,0.0006238299,0.0004527958],"category_scores_gemma":[0.01283696,0.0001970292,0.0007092755,0.001374877,0.0003146647,0.0006069384,0.0005428329,0.001164417,0.0002327175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007698811,"about_ca_system_score_gemma":0.0009445831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006381196,"about_ca_topic_score_gemma":0.004704366,"domain_scores_codex":[0.9979203,0.001247922,0.000132589,0.0002586086,0.0003088255,0.0001317423],"domain_scores_gemma":[0.9927813,0.005692976,0.0004971732,0.0003471557,0.0005572714,0.0001240229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003613779,0.0003890152,0.1764783,0.0002489277,0.0004872596,0.0002602235,0.0001758429,0.4160175,0.001338589,0.003835348,0.006637971,0.3937697],"study_design_scores_gemma":[0.00002444456,0.0001800627,0.0190715,0.00005686788,0.0000557419,0.0001293884,0.00005664607,0.9704861,0.001403485,0.006539769,0.001967585,0.00002834561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6920894,0.01516538,0.279485,0.005466655,0.000290121,0.0001712114,0.002357652,0.001043281,0.003931263],"genre_scores_gemma":[0.9592763,0.001081385,0.03761965,0.0002126332,0.0001183891,0.00005612654,0.001078828,0.0000219237,0.0005348581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006381196,"threshold_uncertainty_score":0.02920592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314317151140253,"score_gpt":0.2796013197591102,"score_spread":0.2664581482477076,"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."}}