{"id":"W4413848866","doi":"10.1016/j.knosys.2025.114383","title":"Prostate cancer forecasting in small samples based on lightweight neural networks using ensemble learning","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nipissing University","funders":"National Natural Science Foundation of China","keywords":"Artificial neural network; Ensemble learning; Prostate cancer; Computer science; Artificial intelligence; Machine learning; Ensemble forecasting; Cancer; Medicine; 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":[0.001312713,0.000790515,0.0009452997,0.0005598,0.0003304865,0.0006228431,0.001006228,0.0005556631,0.0007068201],"category_scores_gemma":[0.004714255,0.0003216002,0.0005750459,0.0005754635,0.0002890528,0.001220744,0.0007551829,0.001202144,0.0002405394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006509598,"about_ca_system_score_gemma":0.0007365177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01267161,"about_ca_topic_score_gemma":0.01921002,"domain_scores_codex":[0.9996829,0.00008366995,0.00002133729,0.00009311467,0.00007078888,0.00004816791],"domain_scores_gemma":[0.9984836,0.0008459489,0.0001381336,0.0001918931,0.0002813196,0.00005912676],"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":[0.000103929,0.00006810358,0.004074011,0.00002701237,0.00008230403,0.00008507576,0.00003715384,0.9137672,0.001634593,0.001142088,0.0009487072,0.07802983],"study_design_scores_gemma":[0.000001105312,0.000006110496,0.0001823667,0.000001366439,0.000003273543,0.000002607657,0.000002174627,0.9991203,0.0001349939,0.0004971422,0.00004707283,0.000001512314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2828687,0.001017519,0.7107772,0.0007243077,0.0001584933,0.00005764683,0.0003923296,0.001621827,0.00238202],"genre_scores_gemma":[0.9516272,0.0002854872,0.04599774,0.0001576858,0.00009495817,0.00004368891,0.0006129569,0.000043722,0.001136603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01267161,"threshold_uncertainty_score":0.02519572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2263426364595368,"score_gpt":0.4359370765796723,"score_spread":0.2095944401201355,"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."}}