{"id":"W4323293986","doi":"10.1371/journal.pone.0281074","title":"Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Newborn Screening Ontario; University of Ottawa; Children's Hospital of Eastern Ontario; Ottawa Hospital","funders":"National Institute of Allergy and Infectious Diseases; Fogarty International Center; National Institutes of Health; Bill and Melinda Gates Foundation","keywords":"Metabolomics; Algorithm; Computer science; Gestational age; Bioinformatics; Machine learning; Medicine; Artificial intelligence; Biology; Pregnancy; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02877771,0.001574165,0.0009091971,0.00168231,0.0007138178,0.001832709,0.002160654,0.00149056,0.001117472],"category_scores_gemma":[0.06914951,0.0004853411,0.001201733,0.0009134763,0.0008282256,0.001023771,0.001802355,0.002267936,0.0005461799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00261,"about_ca_system_score_gemma":0.005827693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03702135,"about_ca_topic_score_gemma":0.01801984,"domain_scores_codex":[0.9934309,0.003865679,0.0005769774,0.001099878,0.000762024,0.000264516],"domain_scores_gemma":[0.9610474,0.0270225,0.002005061,0.002131513,0.007422475,0.0003710611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007339263,0.0003951633,0.1440691,0.0002067606,0.0007505463,0.0001651205,0.0002683696,0.681208,0.002459246,0.002295555,0.002734041,0.1647141],"study_design_scores_gemma":[0.00005129237,0.0000987063,0.007592904,0.00005607107,0.00004362681,0.00005312986,0.00005105844,0.9885026,0.001738743,0.001190029,0.0006037384,0.00001812607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3500225,0.001170972,0.6410434,0.001055306,0.0001220061,0.0006249821,0.001347991,0.002413791,0.002198897],"genre_scores_gemma":[0.7756312,0.000267488,0.2196146,0.0003399154,0.00003425034,0.0006132853,0.002565229,0.0001411228,0.0007929315],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03702135,"threshold_uncertainty_score":0.1521929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2356277752687651,"score_gpt":0.3758761966978088,"score_spread":0.1402484214290437,"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."}}