{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001796511,0.001705472,0.001055875,0.003599997,0.0006424314,0.001684693,0.002451979,0.001086668,0.02552073],"category_scores_gemma":[0.006824757,0.0007061095,0.001111848,0.003457426,0.000383012,0.001466128,0.002964406,0.001489311,0.04197512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000898794,"about_ca_system_score_gemma":0.002430973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01115161,"about_ca_topic_score_gemma":0.02267081,"domain_scores_codex":[0.9985561,0.0002466074,0.0002852189,0.0002909592,0.0004562251,0.0001648513],"domain_scores_gemma":[0.9975,0.0005358248,0.0002466713,0.0008716169,0.0005861804,0.0002597469],"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":[0.000571422,0.000103902,0.002646965,0.001176085,0.00009948708,0.000111959,0.0001015726,0.0008471399,0.002306829,0.001805458,0.9737415,0.01648761],"study_design_scores_gemma":[0.0007244102,0.0001279573,0.01081413,0.0002953917,0.00008410405,0.0003879105,0.0001753789,0.006755383,0.007153622,0.004849335,0.9685361,0.00009628353],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001383812,0.0001399788,0.003811824,0.000182691,0.00007138587,0.0001568143,0.979836,0.01268254,0.001734953],"genre_scores_gemma":[0.00163762,0.00007167662,0.004544234,0.00008075752,0.00001144191,0.0002456326,0.9923084,0.0003447692,0.0007554949],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02552073,"threshold_uncertainty_score":0.08537537,"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."}}