{"id":"W2892262505","doi":"10.1101/413716","title":"Machine Learning to Predict Osteoporotic Fracture Risk from Genotypes","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; McGill University; Jewish General Hospital","funders":"","keywords":"FRAX; Medicine; Osteoporosis; Decile; Femoral neck; Internal medicine; Hip fracture; Bone mineral; Osteoporotic fracture; Machine learning; Oncology; Statistics; Computer science; Mathematics","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":[0.003874355,0.00103661,0.00125122,0.001716256,0.0003067474,0.0009399112,0.0009394067,0.001083287,0.001172931],"category_scores_gemma":[0.01593868,0.0003438683,0.0008531546,0.001141277,0.0005010983,0.0005247161,0.0005427398,0.001229344,0.0007632319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005991426,"about_ca_system_score_gemma":0.0007313413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004240631,"about_ca_topic_score_gemma":0.00199847,"domain_scores_codex":[0.997979,0.001184209,0.0001345953,0.0004146405,0.000159129,0.0001285115],"domain_scores_gemma":[0.9895862,0.008634904,0.0006567782,0.0004291481,0.0005549619,0.0001381946],"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":[0.000652049,0.0005828916,0.1588697,0.000286745,0.001089217,0.0002747373,0.000108441,0.5765457,0.001642947,0.002082374,0.005704637,0.2521606],"study_design_scores_gemma":[0.0000525019,0.00009560728,0.01171476,0.0000422641,0.00006385511,0.00008178309,0.00001828841,0.9778145,0.0005338983,0.008914392,0.0006453218,0.00002275066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5135406,0.00771256,0.4660115,0.003167202,0.0003505094,0.0002535828,0.003658381,0.001998463,0.00330715],"genre_scores_gemma":[0.9377983,0.0006449567,0.05679174,0.000475471,0.0002426673,0.0001970598,0.00286143,0.00004014761,0.0009481272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004240631,"threshold_uncertainty_score":0.02048981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01432243768613017,"score_gpt":0.2624142882528404,"score_spread":0.2480918505667102,"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."}}