{"id":"W3130115236","doi":"10.1007/s00198-021-05896-5","title":"Using 3D image registration to maximize the reproducibility of longitudinal bone strength assessment by HR-pQCT and finite element analysis","year":2021,"lang":"en","type":"article","venue":"Osteoporosis International","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reproducibility; Quantitative computed tomography; Biomedical engineering; Standard deviation; Medicine; Nuclear medicine; Standard error; Osteoporosis; Bone density; Mathematics; Statistics; Pathology","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":[0.005525896,0.00104878,0.001151266,0.001841012,0.0005704307,0.001878603,0.001147016,0.001087,0.002390239],"category_scores_gemma":[0.01351997,0.001058662,0.0008795341,0.00176477,0.0007428663,0.001106622,0.00145122,0.0009036586,0.0009889747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003758363,"about_ca_system_score_gemma":0.001660331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00206022,"about_ca_topic_score_gemma":0.005689852,"domain_scores_codex":[0.9967495,0.001092882,0.0003331757,0.0006362484,0.001060663,0.0001274089],"domain_scores_gemma":[0.9953276,0.001643446,0.0004332418,0.001368538,0.001173183,0.00005396432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001217008,0.0006598021,0.02086459,0.000651964,0.0004577849,0.0002125869,0.0007771233,0.03815638,0.5592424,0.007224827,0.002109611,0.3684259],"study_design_scores_gemma":[0.0002383603,0.0009457879,0.06612813,0.0001029146,0.0006643374,0.001845124,0.0002136419,0.4798806,0.4324734,0.006149889,0.01108785,0.0002700221],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06136149,0.0002822882,0.9342283,0.00007786808,0.0000750453,0.0002355428,0.0001294299,0.001956948,0.001653099],"genre_scores_gemma":[0.2728571,0.0001454288,0.7244576,0.00007420648,0.00002160411,0.0004173912,0.0001828327,0.0009017812,0.0009420257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005525896,"threshold_uncertainty_score":0.0292241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04856742519825129,"score_gpt":0.3890606077865129,"score_spread":0.3404931825882616,"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."}}