{"id":"W4416780642","doi":"10.1016/j.esmorw.2025.100355","title":"158eP Clustering and response prediction in differentiated thyroid cancer: Insights from the Hungarian thyroid cancer register","year":2025,"lang":"en","type":"article","venue":"ESMO Real World Data and Digital Oncology","topic":"Thyroid Cancer Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Cilag; Astex Pharmaceuticals; Loxo Oncology; Institut Gustave-Roussy; Genentech; BeiGene; Eisai Canada; Centre Léon Bérard; Daiichi Sankyo Europe; Servier; Basilea Pharmaceutica; Eisai; Boston Pharmaceuticals; Clovis Oncology; Les Laboratories Pierre Fabre; Bayer HealthCare; Exelixis; PharmaMar; Celgene; Bristol-Myers Squibb; Eli Lilly and Company; AstraZeneca; Chugai Pharmaceutical; Agios Pharmaceuticals; Amgen","keywords":"Thyroid cancer; Cluster analysis; Thyroid; Register (sociolinguistics); Cancer","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001322699,0.0002151079,0.0005578391,0.003161245,0.0003890091,0.001078204,0.0005321344,0.0005535234,0.004462466],"category_scores_gemma":[0.008030267,0.0002391051,0.0008040372,0.006451555,0.0003030566,0.0005754613,0.001186945,0.0004361624,0.0009801507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295202,"about_ca_system_score_gemma":0.0015556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0611142,"about_ca_topic_score_gemma":0.04126339,"domain_scores_codex":[0.9985903,0.0004095004,0.0001795733,0.0002841988,0.0001990361,0.0003374304],"domain_scores_gemma":[0.9964535,0.001566024,0.000755358,0.0004230138,0.0006003734,0.0002017869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003574213,0.00004490982,0.9817509,0.00007684244,0.0002153538,0.0002264473,0.0001962506,0.001394023,0.0001233415,0.0003466496,0.003898651,0.01136919],"study_design_scores_gemma":[0.0000171576,0.00002701996,0.9937906,0.00003303909,0.00008959143,0.0002301542,0.0004606569,0.00270833,0.000115934,0.0002494582,0.002266384,0.00001168004],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9668134,0.0008874364,0.0006285894,0.0007586379,0.0000253933,0.00002548559,0.02864986,0.00003727731,0.002173851],"genre_scores_gemma":[0.9714418,0.0003096871,0.0003573485,0.00009360116,0.00002375019,0.00002625003,0.02706546,0.00001837514,0.0006636342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0611142,"threshold_uncertainty_score":0.1215169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03317932006294425,"score_gpt":0.3352828302058616,"score_spread":0.3021035101429174,"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."}}