{"id":"W3045170854","doi":"10.1101/2020.07.21.20158196","title":"Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Newborn Screening Ontario; Ottawa Public Health; University of Ottawa; Children's Hospital of Eastern Ontario; Ottawa Hospital","funders":"National Institutes of Health; Ottawa Hospital Research Institute; Bill and Melinda Gates Foundation","keywords":"Medicine; Gestational age; Heel; Cord blood; Obstetrics; Cohort; Algorithm; Pregnancy; Cohort study; Computer science; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0347517,0.0012674,0.000797265,0.001247771,0.0005335016,0.001603016,0.001740818,0.001064652,0.001004785],"category_scores_gemma":[0.05845901,0.0004321243,0.001045894,0.0007964115,0.000759011,0.0008528189,0.001867759,0.001928151,0.0005394714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001371291,"about_ca_system_score_gemma":0.002910207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01172705,"about_ca_topic_score_gemma":0.006436391,"domain_scores_codex":[0.9922891,0.005276007,0.0004933182,0.001015598,0.0006919344,0.0002341038],"domain_scores_gemma":[0.9659579,0.02341416,0.001994129,0.002871477,0.005426765,0.0003357406],"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":[0.001441645,0.0006819558,0.2601517,0.0002399373,0.001285341,0.0001923915,0.0003738941,0.5501696,0.004871045,0.002455487,0.003743055,0.174394],"study_design_scores_gemma":[0.00007202672,0.0002092359,0.01798799,0.00007108385,0.00007467798,0.00006929675,0.00006756096,0.9758962,0.003469237,0.001159268,0.0009003259,0.0000230184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5487115,0.001092169,0.4429331,0.0008650937,0.0001321664,0.000619798,0.001701883,0.001940005,0.00200422],"genre_scores_gemma":[0.8645947,0.0001855355,0.1309052,0.0002399497,0.00002864883,0.0004840041,0.002755941,0.000125999,0.0006799882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0347517,"threshold_uncertainty_score":0.1837867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1691754252290577,"score_gpt":0.3932787922739643,"score_spread":0.2241033670449066,"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."}}