{"id":"W3126574438","doi":"10.1016/j.bone.2021.115880","title":"Multisite longitudinal calibration of HR-pQCT scanners and precision in osteogenesis imperfecta","year":2021,"lang":"en","type":"article","venue":"Bone","topic":"Connective tissue disorders research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Shriners Hospitals for Children - Canada; Montreal Children's Hospital","funders":"Shriners Hospitals for Children","keywords":"Imaging phantom; Reproducibility; Scanner; Quantitative computed tomography; Calibration; Accuracy and precision; Nuclear medicine; Biomedical engineering; Osteogenesis imperfecta; Computer science; Medicine; Mathematics; Bone density; Artificial intelligence; Statistics; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001182293,0.00006194077,0.0001020483,0.00004829155,0.00002051613,0.000009437526,0.00003953239,0.00006475901,0.00004566848],"category_scores_gemma":[0.0002240137,0.00006660989,0.00002806949,0.0001295976,0.00003863775,0.000003854789,0.0001126919,0.00003620232,0.00000115943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008515511,"about_ca_system_score_gemma":0.0000411685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002329227,"about_ca_topic_score_gemma":0.001907037,"domain_scores_codex":[0.9993613,0.00007073158,0.0001186698,0.0002396673,0.00009528943,0.0001143083],"domain_scores_gemma":[0.9996768,0.00002084503,0.00002623819,0.000161717,0.00008071988,0.00003364841],"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":[0.0000878651,0.00003839152,0.1599097,0.00001884181,0.00001078653,0.00001109771,0.00004008013,0.00002414613,0.8270718,0.000009263384,0.0000696769,0.01270837],"study_design_scores_gemma":[0.0006784418,0.0001400099,0.2695519,0.00002023321,0.000005085718,0.00001781246,0.0001201317,0.0004631074,0.7285052,0.00002583974,0.0003838052,0.00008847273],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994916,0.0042116,0.0004454446,0.0001040604,0.00001842687,0.0001124774,0.00001409554,0.00000177275,0.0001761097],"genre_scores_gemma":[0.9989291,0.0004122144,0.0002478436,0.00001025648,0.00001940728,0.000009444519,0.00006518172,0.00000874337,0.0002977389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1096422,"threshold_uncertainty_score":0.2716272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409195382701232,"score_gpt":0.2897725440670764,"score_spread":0.275680590240064,"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."}}