{"id":"W4399612193","doi":"10.32614/cran.package.naryn","title":"naryn: Native Access Medical Record Retriever for High Yield Analytics","year":2022,"lang":"en","type":"dataset","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytics; Labrador Retriever; Yield (engineering); Computer science; Medicine; Data science; Surgery; Materials science","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":["metaresearch","metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.002574147,0.0004726444,0.0009997298,0.0004084936,0.001961634,0.0000292576,0.002243508,0.001984935,0.5829073],"category_scores_gemma":[0.01603034,0.0004178452,0.0002107054,0.0008416857,0.000163674,0.0002042291,0.001692346,0.005149757,0.0005541815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032653,"about_ca_system_score_gemma":0.004227563,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05048494,"about_ca_topic_score_gemma":0.07686125,"domain_scores_codex":[0.9934721,0.0008949875,0.001995027,0.0009581716,0.001544809,0.001134913],"domain_scores_gemma":[0.9843265,0.01210285,0.0009440364,0.001187699,0.0008666957,0.0005722511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002369731,0.00007969721,0.0005222891,0.001072341,0.00008451811,0.00004760496,0.0001557613,0.000001709733,3.890006e-8,0.0007292568,0.9956613,0.001408532],"study_design_scores_gemma":[0.0001272661,0.0003196367,0.00002785416,0.0003950194,0.00008395242,0.000001473744,0.002000989,0.0001950908,0.000006021655,0.003297196,0.9931089,0.0004366577],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008045166,0.00008842705,0.000396299,0.01251604,0.01066973,0.003283521,0.9719791,0.0001381789,0.0008482217],"genre_scores_gemma":[0.00007225886,0.001048369,0.0001384943,0.01865344,0.002304182,0.001622936,0.9671892,0.00007522779,0.008895918],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5823531,"threshold_uncertainty_score":0.9998273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3517801724798668,"score_gpt":0.5593725950408152,"score_spread":0.2075924225609484,"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."}}