{"id":"W4312217488","doi":"10.30574/wjarr.2022.16.3.1127","title":"Auto machine learning to predict pregnancy after fresh embryo transfer following in vitro fertilization","year":2022,"lang":"en","type":"article","venue":"World Journal of Advanced Research and Reviews","topic":"Assisted Reproductive Technology and Twin Pregnancy","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"In vitro fertilisation; Embryo transfer; Pregnancy; Construct (python library); Transfer of learning; Machine learning; Live birth; Artificial intelligence; Computer science; Reproduction; Pregnancy rate; Outcome (game theory); Obstetrics; Medicine; Biology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001935118,0.0003829134,0.0003743751,0.0009650338,0.000154489,0.0005603614,0.0004335017,0.0004302949,0.001386061],"category_scores_gemma":[0.007522665,0.0001378903,0.0004574201,0.0005745224,0.0001555003,0.000337583,0.0003570995,0.0006601966,0.0003657502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003504981,"about_ca_system_score_gemma":0.0005733015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003605182,"about_ca_topic_score_gemma":0.003292331,"domain_scores_codex":[0.9993788,0.0002846362,0.00006010513,0.0001244724,0.0001012053,0.00005074535],"domain_scores_gemma":[0.995981,0.002971282,0.000379227,0.0002061845,0.0003605611,0.0001017274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006569796,0.0004524592,0.6583517,0.0001739047,0.0003128306,0.0001922049,0.000108689,0.101118,0.00116265,0.0003532561,0.003191977,0.2339254],"study_design_scores_gemma":[0.00002367972,0.0004539626,0.1836452,0.00007012323,0.0001166253,0.0003089556,0.00009161113,0.8099458,0.001957989,0.001679169,0.001677625,0.00002925187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9493726,0.002439373,0.04133046,0.0007222896,0.0001448667,0.0001478903,0.002909881,0.0005743266,0.00235831],"genre_scores_gemma":[0.9884431,0.0002531821,0.008987125,0.000056869,0.00003758173,0.000048643,0.001497937,0.00001425362,0.0006613471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003605182,"threshold_uncertainty_score":0.01023406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03674244829738158,"score_gpt":0.3470473855643368,"score_spread":0.3103049372669552,"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."}}