{"id":"W4367834427","doi":"10.1002/jbmr.4808","title":"A Fracture Risk Assessment Tool for High Resolution Peripheral Quantitative Computed Tomography","year":2023,"lang":"en","type":"article","venue":"Journal of Bone and Mineral Research","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University Health Network; McGill University Health Centre; Alberta Bone and Joint Health Institute; University of Calgary","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; University of Calgary; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institutes of Health; Institute of Musculoskeletal Health and Arthritis; Amgen","keywords":"Quantitative computed tomography; FRAX; Osteoporosis; Bone mineral; Medicine; Risk assessment; Femoral neck; Osteoporotic fracture; Bone density; Fracture (geology); Prospective cohort study; Nuclear medicine; Internal medicine; Computer science; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003962396,0.001036656,0.0007238714,0.002138632,0.0003815559,0.0008881531,0.000869215,0.0007278839,0.003634372],"category_scores_gemma":[0.01651051,0.0004254963,0.001705085,0.001167894,0.0002671171,0.0006092361,0.0009497757,0.0007535445,0.001258346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003606681,"about_ca_system_score_gemma":0.0009677183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004509442,"about_ca_topic_score_gemma":0.004493004,"domain_scores_codex":[0.998475,0.0005277048,0.0001377006,0.0002319651,0.0005687936,0.0000587253],"domain_scores_gemma":[0.9955593,0.002466157,0.0006530154,0.0003153764,0.0008732089,0.0001329275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001132064,0.0004869279,0.2541659,0.0005539917,0.0007447842,0.0006648398,0.0002403945,0.05979372,0.008842165,0.004662547,0.02013156,0.6485811],"study_design_scores_gemma":[0.0002292964,0.0009181318,0.1557122,0.0004290683,0.000452746,0.004313013,0.0001227038,0.7950717,0.008295441,0.01144037,0.02278322,0.0002321806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.113045,0.001454992,0.8642101,0.0006951742,0.0001688689,0.0009281638,0.007877991,0.008767517,0.002852092],"genre_scores_gemma":[0.4666017,0.0004618339,0.5252557,0.0002830557,0.00009522548,0.001074715,0.004511895,0.0002360207,0.001479992],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004509442,"threshold_uncertainty_score":0.02095538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0698579254909788,"score_gpt":0.4434527866808302,"score_spread":0.3735948611898514,"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."}}