{"id":"W4362632104","doi":"10.17615/s2x3-5k76","title":"Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers","year":2023,"lang":"en","type":"article","venue":"UNC Libraries","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Bill and Melinda Gates Foundation","keywords":"Computer science; Metabolomics; Algorithm; Estimation; Gestational age; Machine learning; Artificial intelligence; Bioinformatics; Pregnancy; Biology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.02371638,0.001644252,0.000952645,0.001539977,0.0006986717,0.001846178,0.002041669,0.001388308,0.001094362],"category_scores_gemma":[0.06163856,0.0004695844,0.001215805,0.0009395467,0.0008263423,0.0009927872,0.001803492,0.002061159,0.000573219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002436806,"about_ca_system_score_gemma":0.00580978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04552249,"about_ca_topic_score_gemma":0.02212246,"domain_scores_codex":[0.9941612,0.003266869,0.0005271212,0.001115334,0.0006603157,0.0002691427],"domain_scores_gemma":[0.9682962,0.02149254,0.001701656,0.001901247,0.006260655,0.0003477128],"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":[0.0007562322,0.0003313692,0.1506337,0.0002208372,0.0007767733,0.0001935534,0.0002748182,0.6636012,0.002860501,0.002413752,0.002932625,0.1750046],"study_design_scores_gemma":[0.00005577335,0.0001073946,0.009836708,0.00006389161,0.0000558598,0.00006747617,0.00005998399,0.9852807,0.002184515,0.001437409,0.0008279907,0.00002232401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3573191,0.001359074,0.6326733,0.001081896,0.0001454085,0.0006388688,0.001598006,0.00273177,0.002452536],"genre_scores_gemma":[0.7861403,0.0003115106,0.2086795,0.00037621,0.00003540344,0.0005909367,0.002813597,0.000149555,0.0009030298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04552249,"threshold_uncertainty_score":0.1254258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4114914882789028,"score_gpt":0.515654931258923,"score_spread":0.1041634429800203,"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."}}