{"id":"W4250713916","doi":"10.22541/au.158204199.92013270","title":"Is it time to screen for cardiometabolic risk factors prior to ART? (Mini-commentary on BJOG-19-1190.R1)","year":2020,"lang":"en","type":"dataset","venue":"Authorea","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Thomas Hospital","funders":"","keywords":"Computer science; Business","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003972992,0.0007203841,0.001607636,0.0006441837,0.0001783372,0.00009570688,0.0003958905,0.0003576353,0.0006601627],"category_scores_gemma":[0.0029602,0.0006530829,0.0007963002,0.0005906885,0.00005287251,0.00004233153,0.0003161359,0.000713251,0.01382638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002135426,"about_ca_system_score_gemma":0.0002071856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001656877,"about_ca_topic_score_gemma":0.00002349996,"domain_scores_codex":[0.9968185,0.0001128428,0.0005633801,0.001042961,0.0007627947,0.0006995281],"domain_scores_gemma":[0.9959261,0.0009972763,0.0001914373,0.00139715,0.0001373924,0.001350631],"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":[0.0003767065,0.0001210548,0.0006934725,0.0001479544,0.000693027,0.00007855902,0.0002951039,0.00001062206,0.000007409677,8.660113e-7,0.9944619,0.003113358],"study_design_scores_gemma":[0.0012233,0.0008605462,0.004903657,0.0003084745,0.002694736,0.00001121653,0.00007666677,0.000009504099,0.0002074861,0.000006821195,0.9890525,0.0006451141],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005200326,0.0001283068,0.0001238104,0.03751878,0.0008403977,0.002368393,0.9577634,0.0001236259,0.0006132629],"genre_scores_gemma":[0.000127942,0.0001129536,0.000808194,0.07841811,0.002462574,0.000215343,0.9155707,0.0001185873,0.002165622],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04219271,"threshold_uncertainty_score":0.9995921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03151690547360256,"score_gpt":0.326105590020113,"score_spread":0.2945886845465104,"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."}}