{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004779069,0.0004338175,0.0006858112,0.001118541,0.0004002378,0.001297692,0.0009489359,0.001198525,0.001359408],"category_scores_gemma":[0.01447393,0.0009271355,0.0004137426,0.001405631,0.0006093427,0.0009326534,0.001038635,0.0008505499,0.0004067974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006105275,"about_ca_system_score_gemma":0.0006774727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004864282,"about_ca_topic_score_gemma":0.005320544,"domain_scores_codex":[0.9967709,0.001282674,0.000317774,0.0007268306,0.0007916555,0.0001101819],"domain_scores_gemma":[0.989952,0.004957989,0.001675858,0.00173231,0.001547377,0.0001344685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003530822,0.0002333459,0.3996971,0.0007065413,0.0004063506,0.000619154,0.001300328,0.04855225,0.2017857,0.001822425,0.001499214,0.3398467],"study_design_scores_gemma":[0.0001071088,0.0007777042,0.7472081,0.0001825363,0.0004892956,0.008221083,0.0004685012,0.1194192,0.1177961,0.00198253,0.003187503,0.0001603483],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7991791,0.009726624,0.1842619,0.000460694,0.0001083626,0.00007398884,0.0009274299,0.001328399,0.003933457],"genre_scores_gemma":[0.9597734,0.0004566855,0.0386814,0.00006036052,0.00002157482,0.00002731308,0.0001508194,0.0002119701,0.0006165416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004864282,"threshold_uncertainty_score":0.02527446,"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."}}