{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002149048,0.0001487143,0.000597068,0.00100583,0.0002770854,0.00001215315,0.0001657258,0.00004462699,0.0001652278],"category_scores_gemma":[0.000728803,0.0001093309,0.0001688277,0.001455725,0.00008639588,0.0001720481,0.00013675,0.002040192,0.000003865654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001625767,"about_ca_system_score_gemma":0.00009632601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002618536,"about_ca_topic_score_gemma":0.00002152967,"domain_scores_codex":[0.9974887,0.0005737062,0.000674684,0.0003241023,0.0005449098,0.0003939055],"domain_scores_gemma":[0.9992354,0.00009045798,0.00009417972,0.0002494455,0.0001430662,0.0001874572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.008460657,0.001080802,0.06410276,0.001050357,0.0002187163,0.002805317,0.002103801,0.0003709401,0.2035275,0.0001162117,0.000642643,0.7155203],"study_design_scores_gemma":[0.01684341,0.01150491,0.1459245,0.02805491,0.0003759068,0.001855806,0.002452638,0.0003513558,0.03123917,0.00303628,0.7573236,0.001037462],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.4320549,0.5592613,0.001068288,0.004291527,0.0004208462,0.001992262,0.000005140403,0.00003803867,0.0008676561],"genre_scores_gemma":[0.9779882,0.014195,0.002338203,0.0001058205,0.00006932883,0.0001993596,0.000006181827,0.00002695648,0.005070894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.756681,"threshold_uncertainty_score":0.8863731,"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."}}