{"id":"W4402164554","doi":"10.1093/jamia/ocae220","title":"Foundation model-driven distributed learning for enhanced retinal age prediction","year":2024,"lang":"en","type":"article","venue":"Journal of the American Medical Informatics Association","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Economic shortage; Fundus (uterus); Machine learning; Biobank; Deep learning; Linear regression; Retinal; Bioinformatics; Medicine; Ophthalmology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001097049,0.0005061679,0.0007610962,0.0002800283,0.0002899172,0.0005452691,0.001564891,0.000652245,0.001591232],"category_scores_gemma":[0.003068925,0.0002517147,0.0004716024,0.0003011602,0.0004762859,0.0009319457,0.001037632,0.001190695,0.0003635289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007475559,"about_ca_system_score_gemma":0.001153701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008556219,"about_ca_topic_score_gemma":0.007661708,"domain_scores_codex":[0.9997061,0.00007242899,0.0000143771,0.0001003734,0.00005585244,0.00005076347],"domain_scores_gemma":[0.9989857,0.0004998478,0.00009718634,0.0001301326,0.0002267462,0.00006036567],"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":[0.0001259029,0.0000854841,0.00149968,0.00002456777,0.00002581313,0.00005460911,0.00003853206,0.9342256,0.001645831,0.001922791,0.001102291,0.05924896],"study_design_scores_gemma":[0.000004003257,0.00000909738,0.0000475068,6.581133e-7,0.000001667309,0.000003954928,0.000002280298,0.9991248,0.0002005696,0.0005595777,0.00004488614,9.560271e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06440824,0.0002913578,0.932206,0.0002858214,0.00005386392,0.00003485063,0.00008706649,0.00159386,0.001038943],"genre_scores_gemma":[0.9320105,0.00008550649,0.06589563,0.0001447928,0.00003191952,0.00006253021,0.0001778312,0.00004866255,0.001542589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008556219,"threshold_uncertainty_score":0.01701283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00990487732864112,"score_gpt":0.304032777485099,"score_spread":0.294127900156458,"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."}}