{"id":"W4210452204","doi":"10.2196/27691","title":"Primary Care: The Actual Intelligence Required for Artificial Intelligence to Advance Health Care and Improve Health","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Healthcare cost, quality, practices","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Leverage (statistics); Government (linguistics); Health care; Conversation; Stakeholder; Medicine; Medical diagnosis; Nursing; Business; Public relations; Knowledge management; Psychology; Computer science; Artificial intelligence; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006770138,0.0003266293,0.0006989847,0.0001652005,0.003732902,0.00004727393,0.000969824,0.0002257847,0.0001816108],"category_scores_gemma":[0.002603991,0.0002577567,0.00009511141,0.0006173549,0.00029385,0.0004896125,0.001228191,0.002561642,0.00008901682],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002147876,"about_ca_system_score_gemma":0.008378012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005454629,"about_ca_topic_score_gemma":0.0009733785,"domain_scores_codex":[0.9918483,0.001747452,0.002913507,0.0003851166,0.001704059,0.001401486],"domain_scores_gemma":[0.9924146,0.00371251,0.001309379,0.0008224065,0.0004467985,0.00129431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002325601,0.00005533061,0.00006870203,0.004877886,0.00001297049,0.000001383005,0.2941314,0.00001572757,9.634621e-7,0.01016519,0.004567124,0.6858707],"study_design_scores_gemma":[0.0001892099,0.002231235,0.0001271911,0.0004305168,0.000009305521,0.00001149731,0.6188967,0.001411718,0.0000172827,0.001504309,0.3748473,0.00032372],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.09964041,0.01328505,0.376458,0.4505484,0.01182579,0.04116127,0.00274394,0.001027227,0.003309841],"genre_scores_gemma":[0.4409137,0.002010842,0.03628466,0.5088314,0.00165463,0.009220779,0.0007485301,0.000137849,0.0001975843],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.685547,"threshold_uncertainty_score":0.9999875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3390967474766703,"score_gpt":0.5433301557162067,"score_spread":0.2042334082395363,"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."}}