{"id":"W3038032928","doi":"10.1016/s2589-7500(20)30131-x","title":"Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study","year":2020,"lang":"en","type":"article","venue":"The Lancet Digital Health","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"Basic Energy Sciences; Office of Science; National Institute for Health and Care Research; Bill and Melinda Gates Foundation; University of Oxford; U.S. Department of Energy; European Research Council; National Institutes of Health; National Science Foundation","keywords":"Gestational age; Fetus; Medicine; Context (archaeology); Obstetrics; Population; Gestation; Pregnancy; Confidence interval; Prospective cohort study; Generation R; Small for gestational age; Internal medicine; Biology; Environmental health","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":[0.0003624647,0.0001544608,0.0004932271,0.00005268299,0.0001826504,0.00004083738,0.0002236874,0.0000210957,0.00001615303],"category_scores_gemma":[0.0006484956,0.0001130524,0.00003346767,0.000147154,0.00008202231,0.0003158546,0.0001072267,0.0002588764,7.644545e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000040815,"about_ca_system_score_gemma":0.0001295871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000912512,"about_ca_topic_score_gemma":0.0002378329,"domain_scores_codex":[0.9985254,0.0001398963,0.0003552506,0.0003639232,0.0004665983,0.0001489405],"domain_scores_gemma":[0.9987247,0.0005530135,0.0002581463,0.0002858436,0.00008724308,0.00009104549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001296692,0.0006602511,0.9937239,0.00009488285,0.0002585585,0.00001011989,0.002815989,0.0006708939,0.000005729481,0.00003654819,0.00001626018,0.0004101692],"study_design_scores_gemma":[0.002752829,0.001725874,0.8537846,0.0001602806,0.00008626642,0.000001221946,0.0009881875,0.1403594,0.000002889246,0.00006684798,0.000002538064,0.00006907518],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917484,0.0001058604,0.0007454925,0.002943026,0.00004385504,0.001417242,0.002549766,0.00007086985,0.000375522],"genre_scores_gemma":[0.9960988,0.00001179425,0.0004527085,0.0003274232,0.000129017,0.00003760848,0.002922279,0.00001626787,0.000004102937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1399393,"threshold_uncertainty_score":0.4610141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0749217822808317,"score_gpt":0.3588375866383072,"score_spread":0.2839158043574755,"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."}}