{"id":"W4381734045","doi":"10.1093/humrep/dead093.101","title":"O-087 Patient-centric, machine learning (ML)-based personalised prognostics supports fertility specialists to improve access to assisted reproductive technology (ART) and increase overall live birth (LB) outcomes","year":2023,"lang":"en","type":"article","venue":"Human Reproduction","topic":"Assisted Reproductive Technology and Twin Pregnancy","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CReATe Fertility Centre; Translational Research in Oncology","funders":"","keywords":"Prognostics; Assisted reproductive technology; Fertility; Infertility; Reproductive medicine; Cohort; Medicine; Computer science; Population; Pregnancy; Environmental health; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009967858,0.000554709,0.0008637999,0.001497057,0.0006590408,0.00008463096,0.0002678905,0.0003448824,0.0001784352],"category_scores_gemma":[0.007176846,0.0005177866,0.0001663764,0.002062821,0.0003981498,0.0002547877,0.0005543202,0.0009752716,0.0001656384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003483923,"about_ca_system_score_gemma":0.0001654948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001664848,"about_ca_topic_score_gemma":0.00008268053,"domain_scores_codex":[0.9944901,0.0002299924,0.0007814406,0.003147178,0.0005827401,0.0007685657],"domain_scores_gemma":[0.9963002,0.00008417947,0.0004182004,0.002048498,0.0008106866,0.0003382862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00180531,0.001038218,0.9269707,0.0003523844,0.0003642254,0.0004159046,0.001169727,0.00006816076,0.02219505,0.0002080664,0.002155907,0.04325631],"study_design_scores_gemma":[0.001898995,0.001437987,0.9603038,0.0002154099,0.0003047669,0.0001438311,0.0004412145,0.0000566017,0.02759687,0.0006434688,0.006381933,0.0005751235],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783274,0.0003492347,0.0001027396,0.01459645,0.0007964126,0.003550819,0.00005314417,0.001893223,0.0003306096],"genre_scores_gemma":[0.9923846,0.00002209091,0.0009642263,0.0003448291,0.000419521,0.0004715832,0.0003569173,0.00009819597,0.00493808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04268119,"threshold_uncertainty_score":0.9997274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02573090087572455,"score_gpt":0.3028789259772804,"score_spread":0.2771480251015558,"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."}}