{"id":"W1997823434","doi":"10.1016/s1361-8415(02)00133-0","title":"Robust registration for computer-integrated orthopedic surgery: Laboratory validation and clinical experience","year":2003,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Outlier; Artificial intelligence; Computer science; Computer vision; Orthopedic surgery; Spurious relationship; Image registration; Process (computing); Image (mathematics); Medicine; Surgery; Machine learning","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":[0.000931765,0.0001167907,0.0003122585,0.0001564938,0.00006703388,0.00007450102,0.00005406358,0.0001394645,0.0001389785],"category_scores_gemma":[0.00101934,0.0001068108,0.0001344988,0.000826841,0.00009720067,0.0001332637,0.000005300712,0.0001135506,0.000003467627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002444351,"about_ca_system_score_gemma":0.00005955589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001730427,"about_ca_topic_score_gemma":0.00004905149,"domain_scores_codex":[0.9986847,0.0001390838,0.0004998417,0.0002339448,0.0002833844,0.0001590435],"domain_scores_gemma":[0.9990459,0.0003472231,0.00006358981,0.0001744881,0.0001830231,0.0001857588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006047025,0.0005556577,0.3065415,0.0004206263,0.0030958,0.0001408919,0.0005938522,0.5660938,0.0007641009,0.00156874,0.03961871,0.08054586],"study_design_scores_gemma":[0.0001988928,0.00001948482,0.001917524,0.00001496214,0.0002988923,0.000001304955,0.00004654221,0.9948421,0.000412167,0.000009867309,0.00210338,0.0001348558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1779542,0.00006433787,0.821524,0.00008343444,0.0001556029,0.00007612541,0.000007826265,0.00008319558,0.00005133763],"genre_scores_gemma":[0.9742258,0.0004084643,0.02460488,0.0001909666,0.0001533234,0.00001757331,0.0003346275,0.00002362874,0.00004069239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.796919,"threshold_uncertainty_score":0.4355617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02871522958399645,"score_gpt":0.279519447161338,"score_spread":0.2508042175773416,"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."}}