{"id":"W4414799453","doi":"10.21203/rs.3.rs-7713694/v1","title":"Lack of children in public medical imaging data points to growing age bias in biomedical AI","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children","funders":"National Institute of Biomedical Imaging and Bioengineering; Advanced Research Projects Agency; National Institutes of Health; Alliance de recherche numérique du Canada; Hospital for Sick Children","keywords":"Medical imaging; Equity (law); Scarcity; Public health; MEDLINE; Data collection","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01430903,0.0002643984,0.0005478134,0.003201641,0.000997046,0.002269295,0.001392598,0.001260475,0.01253103],"category_scores_gemma":[0.1465571,0.0003873969,0.0004258117,0.003466731,0.001574241,0.003481928,0.001765333,0.001313081,0.00206494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000989476,"about_ca_system_score_gemma":0.00137668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.016257,"about_ca_topic_score_gemma":0.01279135,"domain_scores_codex":[0.9890873,0.004247135,0.001486182,0.002126944,0.002319855,0.0007324734],"domain_scores_gemma":[0.7972492,0.1129504,0.04002859,0.02495206,0.02170278,0.003117035],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000256255,0.00006577698,0.9388725,0.0001803542,0.0001014647,0.0003032521,0.002943904,0.0002161505,0.001318002,0.003998687,0.007599298,0.04414443],"study_design_scores_gemma":[0.00002931558,0.0001332812,0.9266737,0.0004934818,0.0001263257,0.002517012,0.006655621,0.00123537,0.004853559,0.01748277,0.03974512,0.00005447206],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9034971,0.00319015,0.01612739,0.02736581,0.0003867054,0.00008483044,0.01596484,0.0002861289,0.03309715],"genre_scores_gemma":[0.9860161,0.00087469,0.003812565,0.003172729,0.0001884199,0.00007007901,0.003310137,0.0001526042,0.002402711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.985691,"threshold_uncertainty_score":0.07567424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5673570396293318,"score_gpt":0.6029088406814784,"score_spread":0.03555180105214661,"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."}}