{"id":"W3022078172","doi":"10.2196/15411","title":"Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Prospective cohort study; Predictive modelling; Preeclampsia; Intrauterine growth restriction; Medicine; Pregnancy; Internal medicine; Gestation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002206947,0.000136236,0.0003932478,0.00007157234,0.00006485632,0.000004178954,0.00006872068,0.0001248884,0.00002854614],"category_scores_gemma":[0.0002313061,0.0001053772,0.00003939074,0.0002142128,0.000141339,0.0001477553,0.0001098538,0.000403293,0.000002116723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003247734,"about_ca_system_score_gemma":0.0001355315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003920019,"about_ca_topic_score_gemma":0.000001060858,"domain_scores_codex":[0.998367,0.00002406186,0.0007188021,0.0001017208,0.0006528929,0.0001355477],"domain_scores_gemma":[0.9993063,0.00006661475,0.0002216591,0.00009564102,0.0001148466,0.0001949067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003054236,0.000972089,0.7070366,0.01334344,0.001870363,0.00003623997,0.1861786,0.000219986,0.000272779,0.02834967,0.001555541,0.05711041],"study_design_scores_gemma":[0.01071615,0.006384771,0.511567,0.005478207,0.0003908746,0.0001307685,0.004436896,0.4448734,0.00939595,0.003219379,0.002856521,0.0005501362],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9652383,0.0003455621,0.01599908,0.0008780985,0.00007594151,0.001282612,0.00002569777,0.0001427051,0.016012],"genre_scores_gemma":[0.9916956,0.001851826,0.006036443,0.000231444,0.00005410862,0.00007231521,0.00003967249,0.00000885565,0.000009704512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4446534,"threshold_uncertainty_score":0.4297157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536793263062729,"score_gpt":0.2616337559565901,"score_spread":0.2262658233259628,"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."}}