{"id":"W4416133213","doi":"10.1002/ijgo.70657","title":"Machine learning versus traditional formulas for fetal weight estimation: An international multicenter study evaluating prediction accuracy across birth weight percentiles","year":2025,"lang":"en","type":"article","venue":"International Journal of Gynecology & Obstetrics","topic":"Gestational Diabetes Research and Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Percentile; Fetal weight; Multicenter study; Birth weight; Body weight; Low birth weight","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":[0.01546704,0.0008130628,0.0007276706,0.001098251,0.0002112177,0.0006664971,0.0007572998,0.0006433176,0.000634201],"category_scores_gemma":[0.02905578,0.0002378717,0.0006744877,0.001160699,0.0004211309,0.0009130038,0.0006357151,0.000667686,0.0002779478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003926496,"about_ca_system_score_gemma":0.0003119106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00150778,"about_ca_topic_score_gemma":0.001005813,"domain_scores_codex":[0.9951564,0.003079555,0.0003172027,0.0007480297,0.0005973261,0.0001014533],"domain_scores_gemma":[0.9818064,0.009801822,0.004990899,0.001718419,0.001280987,0.0004014001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003882686,0.0002601441,0.9514503,0.0001242057,0.001012124,0.00006235622,0.0001435662,0.002061598,0.0003156371,0.0001136786,0.0007460966,0.03982751],"study_design_scores_gemma":[0.000658154,0.006729486,0.9512334,0.0002972455,0.001470933,0.0008535053,0.0004097447,0.0345865,0.001533037,0.0003769678,0.001805694,0.00004535395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909475,0.003722771,0.003557029,0.000309795,0.00003371831,0.00005884869,0.0008379902,0.00005023089,0.0004820995],"genre_scores_gemma":[0.9956885,0.0007704685,0.002226529,0.0000792181,0.00003665298,0.00005653849,0.001006427,0.00001668179,0.0001190079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01546704,"threshold_uncertainty_score":0.08179855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05605787468338242,"score_gpt":0.4025707823624297,"score_spread":0.3465129076790472,"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."}}