{"id":"W3200448077","doi":"10.29245/2767-5130/2020/3.1119","title":"A more accurate method to determine the magnification of radiographs when templating for hip arthroplasty?","year":2020,"lang":"en","type":"article","venue":"Journal of Orthopedics and Orthopedic Surgery","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Magnification; Radiography; Medicine; Prosthesis; Pelvis; Femoral head; Nuclear medicine; Surgery; Radiology; Orthodontics; Mathematics; Computer science; Artificial intelligence","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":[0.001203694,0.0001864099,0.0004645604,0.0002027384,0.0001024227,0.0000374428,0.0001043669,0.00005803786,0.00001473583],"category_scores_gemma":[0.0005366844,0.0001357622,0.0002419096,0.0003774441,0.00005258225,0.0002876276,0.00003211401,0.0002524928,6.936324e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001015839,"about_ca_system_score_gemma":0.00006305194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002029537,"about_ca_topic_score_gemma":0.000001567307,"domain_scores_codex":[0.9984168,0.00005709871,0.0008633465,0.0001462626,0.0002737123,0.0002427214],"domain_scores_gemma":[0.9979129,0.00111912,0.000382095,0.000131415,0.0002164119,0.0002380391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002396489,0.00003021,0.05936638,0.0005768351,0.0002183733,0.00005596804,0.003100427,0.0160779,0.003324177,0.00004384064,0.005827837,0.9111384],"study_design_scores_gemma":[0.004822133,0.001407895,0.04504139,0.001795899,0.001597207,0.001609272,0.01079054,0.2462301,0.006711938,0.0009161459,0.676708,0.00236953],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4111575,0.00265476,0.5835661,0.001868121,0.000387567,0.0002495044,0.00002840155,0.00003377282,0.00005424725],"genre_scores_gemma":[0.9473663,0.001663794,0.04922953,0.0008165457,0.0008048808,0.00002022967,0.000007667229,0.00006273144,0.00002830907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9087689,"threshold_uncertainty_score":0.553622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03957737705532779,"score_gpt":0.2759311969128025,"score_spread":0.2363538198574747,"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."}}