{"id":"W7125805820","doi":"10.1109/medai67139.2025.00014","title":"Fed-ensemble: Enhancing Federated Learning with Ensemble Models for an Explainable Thyroid Cancer Recurrence Prediction","year":2025,"lang":"","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Generalizability theory; Ensemble learning; Federated learning; Ensemble forecasting; Transparency (behavior); Thyroid cancer; Robustness (evolution)","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":[0.003199381,0.0009617708,0.0014895,0.0007533306,0.0005942788,0.00115084,0.001492128,0.00115583,0.001091399],"category_scores_gemma":[0.006404055,0.0003669646,0.0012058,0.0007350603,0.0004808767,0.001814562,0.001503685,0.002127681,0.0003204852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007729826,"about_ca_system_score_gemma":0.001454841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009295457,"about_ca_topic_score_gemma":0.01024489,"domain_scores_codex":[0.9989185,0.0004286625,0.00005408056,0.0002967801,0.0001815688,0.0001204268],"domain_scores_gemma":[0.9970621,0.001659986,0.0001836209,0.0004541315,0.0005211656,0.0001188947],"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":[0.0002381176,0.000251996,0.007885147,0.00005380706,0.0002606814,0.0001743768,0.000132056,0.8546107,0.001188378,0.004565708,0.003491643,0.1271474],"study_design_scores_gemma":[0.000005465631,0.00002690996,0.0002025028,0.000005578302,0.00001820289,0.00001502202,0.000007658039,0.9962269,0.0002403503,0.002962892,0.0002837228,0.000004724122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07717518,0.001131641,0.9157014,0.001213943,0.0001589762,0.00008289992,0.0004233562,0.002273438,0.001839105],"genre_scores_gemma":[0.8918725,0.0004043177,0.1035243,0.0005387365,0.0001528351,0.000105259,0.0009424192,0.0000992766,0.002360153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009295457,"threshold_uncertainty_score":0.01848274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03027348860040412,"score_gpt":0.3141351028698686,"score_spread":0.2838616142694645,"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."}}