{"id":"W4399150161","doi":"10.1200/jco.2024.42.16_suppl.e13639","title":"Auto-machine learning for opportunistic thyroid nodule detection in lung cancer screening chest CT.","year":2024,"lang":"en","type":"article","venue":"Journal of Clinical Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Medicine; Lung cancer screening; Radiology; Lung cancer; Context (archaeology); Thyroid nodules; Thyroid cancer; Artificial intelligence; National Lung Screening Trial; Colorectal cancer; Machine learning; Thyroid; Nuclear medicine; Cancer; Computed tomography; Computer science; Pathology; Internal medicine","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":[0.003448332,0.0009513266,0.0006102723,0.001395936,0.0002817049,0.000768383,0.001339926,0.001234872,0.001881558],"category_scores_gemma":[0.008861017,0.0003904762,0.0009167237,0.0005162322,0.0003128322,0.0006570385,0.001043257,0.0009851768,0.001058789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008147217,"about_ca_system_score_gemma":0.001136014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007982367,"about_ca_topic_score_gemma":0.01194921,"domain_scores_codex":[0.9989801,0.0004363862,0.00005447217,0.0002830763,0.0001751839,0.00007080451],"domain_scores_gemma":[0.9969562,0.002146766,0.0002027856,0.0002709873,0.0003256341,0.00009762756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001518035,0.00077538,0.09476671,0.0006486762,0.0005903491,0.0006448484,0.0002126818,0.3853016,0.008941176,0.001440443,0.02072949,0.4844306],"study_design_scores_gemma":[0.00003523732,0.0001517801,0.004704836,0.00003567163,0.00004404951,0.0001469172,0.00002746786,0.9891728,0.002603044,0.001292489,0.001769311,0.00001629529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6296032,0.006906514,0.3221506,0.002690604,0.0004120575,0.000817007,0.00597283,0.02165139,0.009795763],"genre_scores_gemma":[0.9191545,0.0004033583,0.07170622,0.0005451652,0.00009743115,0.000235999,0.004625769,0.0003015113,0.002930124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007982367,"threshold_uncertainty_score":0.01823676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08138901625536003,"score_gpt":0.4727752139493028,"score_spread":0.3913861976939427,"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."}}