{"id":"W4410506516","doi":"10.12927/hcpap.2025.27569","title":"Training Data Tell Us a Lot About Whom Health AI Tools Are Likely to Benefit","year":2025,"lang":"en","type":"article","venue":"A Nudge Too Far? A Nudge at All? On Paying People to Be Healthy","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Work & Health","funders":"","keywords":"Training (meteorology); Computer science; Data science; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.05306241,0.001702167,0.002610176,0.004031139,0.002282848,0.007851598,0.002411,0.00519719,0.01263057],"category_scores_gemma":[0.2630853,0.001223897,0.002826595,0.003662055,0.00379181,0.01298132,0.004045074,0.008480912,0.00784104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002343112,"about_ca_system_score_gemma":0.003738679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003873689,"about_ca_topic_score_gemma":0.005620148,"domain_scores_codex":[0.9688056,0.0198132,0.002670758,0.003584457,0.003883568,0.001242404],"domain_scores_gemma":[0.6537673,0.2822862,0.01188304,0.03436155,0.01406011,0.003641727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003759095,0.001347162,0.4255095,0.003489919,0.002623971,0.000404403,0.001936975,0.02743422,0.002566401,0.03098737,0.2385949,0.2613461],"study_design_scores_gemma":[0.001366568,0.001538243,0.2408648,0.009033213,0.002527861,0.001706465,0.006156565,0.09959795,0.007687671,0.3374838,0.2912821,0.0007547831],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3240224,0.0170444,0.1783533,0.1784974,0.004534282,0.001898572,0.2221103,0.002575601,0.07096368],"genre_scores_gemma":[0.7624341,0.004227286,0.0737849,0.03172825,0.00129756,0.00231609,0.1187897,0.0009869146,0.00443534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05306241,"threshold_uncertainty_score":0.2806242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3861112085792702,"score_gpt":0.5145671997662392,"score_spread":0.128455991186969,"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."}}