{"id":"W4405640894","doi":"10.1101/2024.12.16.24318586","title":"Using a Multilingual AI Care Agent to Reduce Disparities in Colorectal Cancer Screening: Higher FIT Test Adoption Among Spanish-Speaking Patients","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of California, Davis","keywords":"Test (biology); Colorectal cancer; Colorectal cancer screening; Medicine; Psychology; Cancer; Political science; Internal medicine; Colonoscopy","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":[0.001071892,0.0001420006,0.0001598446,0.0003064405,0.0006489597,0.0007281364,0.000303341,0.0002375508,0.004544247],"category_scores_gemma":[0.006393631,0.00007663431,0.0002918441,0.000283763,0.0002110321,0.0003788011,0.001184418,0.000447114,0.0003741545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007795124,"about_ca_system_score_gemma":0.001936533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01009062,"about_ca_topic_score_gemma":0.01457087,"domain_scores_codex":[0.9991633,0.0004303856,0.00005110053,0.00008311563,0.00009556684,0.0001765951],"domain_scores_gemma":[0.9973808,0.0005262522,0.0008436959,0.0001010939,0.0003103937,0.0008377432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005015997,0.000966711,0.9302399,0.0001507246,0.00007118682,0.0002855116,0.002744804,0.0001253653,0.0009459388,0.0001786611,0.003038091,0.06075139],"study_design_scores_gemma":[0.0002663152,0.002146535,0.966884,0.0003226082,0.0001726911,0.0006472012,0.01467814,0.001854692,0.001138511,0.0003270645,0.011531,0.00003140036],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945098,0.000256828,0.0003071897,0.001037864,0.00002314959,0.00005763519,0.0001593351,0.00002180298,0.003626478],"genre_scores_gemma":[0.9985616,0.0001240946,0.000332501,0.0004077171,0.00001883222,0.00005535963,0.00007854214,0.000004888543,0.0004165605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01009062,"threshold_uncertainty_score":0.02006376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1922881293518805,"score_gpt":0.4481764435229838,"score_spread":0.2558883141711032,"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."}}