{"id":"W4282929710","doi":"10.2196/33835","title":"The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Preterm Birth and Chorioamnionitis","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; National Health Commission of the People's Republic of China; National Natural Science Foundation of China","keywords":"Medicine; Obstetrics; Machine learning; Medical record; Pregnancy; Gestational age; Computer science; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002364195,0.0003615211,0.0003507907,0.001110141,0.0003907495,0.0005628646,0.0005014674,0.0004878038,0.0006108161],"category_scores_gemma":[0.004578715,0.0004171819,0.00105517,0.001096569,0.0002583266,0.0005440317,0.0003841474,0.0007600406,0.0001590086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003794442,"about_ca_system_score_gemma":0.0005114285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005467797,"about_ca_topic_score_gemma":0.00372688,"domain_scores_codex":[0.99915,0.0002809281,0.0001207923,0.0002284195,0.0001304577,0.00008938504],"domain_scores_gemma":[0.9978072,0.0005871253,0.0006019653,0.0005445392,0.0002628926,0.0001962899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001080514,0.00003888565,0.9982945,0.000007815122,0.0001060464,0.00006535969,0.00003903714,0.0001051129,0.0001132552,0.00002735496,0.00005320739,0.001041369],"study_design_scores_gemma":[0.00002006772,0.0003637169,0.9941518,0.00001695497,0.0001960752,0.0004610956,0.0002277366,0.004037085,0.000168978,0.00009547651,0.000249204,0.00001165795],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985575,0.0002021991,0.0007500495,0.00003258778,0.000008117217,0.00002157983,0.0003255748,0.000004227168,0.00009815519],"genre_scores_gemma":[0.9988601,0.0001594673,0.0003877162,0.00001753854,0.00001030193,0.00003018191,0.0004335427,0.000002065389,0.00009909346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005467797,"threshold_uncertainty_score":0.01250315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008602822621137736,"score_gpt":0.2569040681675534,"score_spread":0.2483012455464157,"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."}}