{"id":"W4386898030","doi":"10.1016/j.ajogmf.2023.101110","title":"Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models","year":2023,"lang":"en","type":"article","venue":"American Journal of Obstetrics & Gynecology MFM","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Metabolomics Innovation Centre","funders":"Horizon 2020; Fetal Medicine Foundation; European Commission","keywords":"Preeclampsia; Biomarker; Medicine; Placental growth factor; Gestation; Uterine artery; Obstetrics; Body mass index; Pregnancy; Blood pressure; Mean arterial pressure; Prospective cohort study; Internal medicine; Biology; Heart rate","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005285867,0.00151835,0.001793388,0.002053321,0.0003732594,0.001846297,0.001124732,0.001181171,0.0007489824],"category_scores_gemma":[0.01218382,0.0003943615,0.0009937893,0.001117002,0.000380027,0.001028189,0.001042398,0.001835926,0.0004120289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007644676,"about_ca_system_score_gemma":0.0009122668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004133112,"about_ca_topic_score_gemma":0.002045315,"domain_scores_codex":[0.9982349,0.0009318836,0.0001353661,0.0003956964,0.0001863337,0.0001157685],"domain_scores_gemma":[0.9934307,0.004862379,0.000619396,0.0002421956,0.000671102,0.0001742621],"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":[0.0007214327,0.0007150379,0.07382945,0.0001471194,0.0007835178,0.0002530181,0.00008170518,0.6497665,0.00212885,0.00106551,0.002728888,0.267779],"study_design_scores_gemma":[0.00001457844,0.0001157976,0.002923286,0.00001542954,0.00006158557,0.00002664925,0.00001230635,0.9948964,0.0002886134,0.001476298,0.000155342,0.0000137902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3510724,0.005017911,0.6342009,0.00285482,0.0002832633,0.0003388029,0.001477904,0.002030413,0.002723683],"genre_scores_gemma":[0.937457,0.0007414653,0.05938852,0.0002671893,0.0002275858,0.0001788885,0.001165725,0.00003768694,0.000535993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005285867,"threshold_uncertainty_score":0.0279547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06159170407919625,"score_gpt":0.2938067732765539,"score_spread":0.2322150691973576,"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."}}