{"id":"W4317039737","doi":"10.18280/ria.360608","title":"Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Diabetic retinopathy; Stacking; Medicine; Ensemble learning; Retinopathy; Pattern recognition (psychology); Ophthalmology; Artificial intelligence; Computer science; Diabetes mellitus; Physics; Endocrinology; Nuclear magnetic resonance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001033087,0.001196651,0.001092565,0.002983381,0.0004603254,0.0008213221,0.0007338295,0.0006419356,0.0008596769],"category_scores_gemma":[0.001498069,0.0002227228,0.001133016,0.001149168,0.0001610761,0.0008029807,0.0006138132,0.0008147349,0.0004854512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004299501,"about_ca_system_score_gemma":0.0005141606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006055722,"about_ca_topic_score_gemma":0.006533373,"domain_scores_codex":[0.9994186,0.0001029504,0.00004792034,0.0001501192,0.000187439,0.00009308082],"domain_scores_gemma":[0.9993068,0.000135491,0.00006831734,0.00009082162,0.0003483322,0.00005028194],"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":[0.0004756637,0.0004345694,0.0412382,0.0001186491,0.0006271995,0.0003130089,0.0001719345,0.1205082,0.01486014,0.000842499,0.01051899,0.8098909],"study_design_scores_gemma":[0.00001201291,0.0001708663,0.01342423,0.00003168295,0.0002622513,0.0002278386,0.00008948429,0.9741967,0.008530422,0.001506347,0.00150745,0.00004061059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5163278,0.004139175,0.4656722,0.0006693053,0.0005307705,0.0001600178,0.001852467,0.004382175,0.006266237],"genre_scores_gemma":[0.9362767,0.0008954788,0.05857773,0.0001195845,0.0001555336,0.0000487048,0.001884111,0.00005822323,0.00198384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006055722,"threshold_uncertainty_score":0.01204091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0807365816351201,"score_gpt":0.3517476744779537,"score_spread":0.2710110928428336,"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."}}