{"id":"W4417445433","doi":"10.51473/rcmos.v1i1.2023.1851","title":"Modelos de inteligência artificial na atenção primária: desempenho, transparência e segurança na triagem de pacientes","year":2023,"lang":"","type":"article","venue":"RCMOS - Revista Científica Multidisciplinar O Saber","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Excellence; Interoperability; Clinical governance; Health care; Primary care; Patient safety; Corporate governance; Clinical decision support system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.007073099,0.001511914,0.002069612,0.001211991,0.002370784,0.0007317453,0.001259915,0.001309391,0.001898772],"category_scores_gemma":[0.003847681,0.001504145,0.001301729,0.005157912,0.0009689696,0.0007596153,0.0004522418,0.002171438,0.005958608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002651827,"about_ca_system_score_gemma":0.003609852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004295018,"about_ca_topic_score_gemma":0.0001818171,"domain_scores_codex":[0.9859252,0.001253181,0.003911298,0.002751593,0.001934161,0.004224589],"domain_scores_gemma":[0.9915684,0.001501813,0.0009762191,0.00228305,0.001415121,0.002255439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008407828,0.008527088,0.0770442,0.009686806,0.001443563,0.001423639,0.2607841,0.008190926,0.2671519,0.006711394,0.0560455,0.294583],"study_design_scores_gemma":[0.002308205,0.002755414,0.0276791,0.008498672,0.003557829,0.0003827305,0.07000206,0.6757659,0.1712631,0.004224644,0.0285273,0.005035008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620548,0.002934353,0.01561853,0.008909547,0.003455916,0.004821077,0.0003326557,0.0008019714,0.001071172],"genre_scores_gemma":[0.9867294,0.002414308,0.001588337,0.0006724076,0.002848414,0.0006069565,0.0005372672,0.0003521453,0.004250715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.667575,"threshold_uncertainty_score":0.9999871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1625026151694274,"score_gpt":0.4155110642776653,"score_spread":0.2530084491082378,"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."}}