{"id":"W2115679306","doi":"10.1016/j.spinee.2007.07.168","title":"142. Clinical Magnification Error in Lateral Spinal Digital Radiographs","year":2007,"lang":"en","type":"article","venue":"The Spine Journal","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Magnification; Medicine; Radiography; Context (archaeology); Perspective (graphical); Digital radiography; Radiology; Medical physics; Orthodontics; Computer vision; Artificial intelligence; Computer science","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":[0.004190185,0.0004421791,0.00035067,0.002793917,0.0007570397,0.001368633,0.0006204498,0.001896746,0.01209879],"category_scores_gemma":[0.05118237,0.0004318147,0.000349318,0.001457288,0.0008403427,0.001138328,0.00096803,0.0006765807,0.002858122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008564967,"about_ca_system_score_gemma":0.0006855877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002191944,"about_ca_topic_score_gemma":0.002280166,"domain_scores_codex":[0.9954336,0.001030389,0.0008952103,0.0003888362,0.002113564,0.0001384048],"domain_scores_gemma":[0.9727116,0.01652291,0.002410251,0.001810693,0.006186354,0.0003581714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.006170717,0.0001541106,0.1270797,0.001381998,0.0001611205,0.0117793,0.0009648533,0.003229571,0.08182638,0.007987164,0.02381634,0.7354488],"study_design_scores_gemma":[0.0003810456,0.001337963,0.5880325,0.001034954,0.0004814451,0.1600679,0.00080397,0.02347393,0.1622966,0.006304857,0.05542507,0.0003598228],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6949259,0.02134988,0.1555643,0.006949155,0.003797857,0.0004393958,0.003227193,0.002623384,0.1111229],"genre_scores_gemma":[0.9513107,0.001789129,0.03617615,0.0007173226,0.0004223354,0.00006457424,0.0004332902,0.0004995856,0.008587018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01209879,"threshold_uncertainty_score":0.04047447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02310930380760114,"score_gpt":0.3198624661478651,"score_spread":0.2967531623402639,"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."}}