{"id":"W2620648587","doi":"10.1017/cjn.2017.60","title":"GP.01 Quantification of computational geometric congruence in surface-based registration for spinal intra-operative three-dimensional navigation","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Spinal Fractures and Fixation Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada); Toronto Public Health","funders":"","keywords":"Congruence (geometry); Medicine; Cervical spine; Planar; Computer vision; Artificial intelligence; Anatomy; Geometry; Computer science; Mathematics; Surgery","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.002161891,0.0005054703,0.0002761666,0.0006857426,0.0003142626,0.001686817,0.0009249732,0.000687206,0.003408915],"category_scores_gemma":[0.01271696,0.0003751458,0.0005332245,0.0006569173,0.000757981,0.0008208624,0.001454372,0.0005188618,0.0007514993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006818401,"about_ca_system_score_gemma":0.001415533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004668648,"about_ca_topic_score_gemma":0.003869768,"domain_scores_codex":[0.9985777,0.0003906145,0.00006666682,0.0001660921,0.0007482178,0.00005072958],"domain_scores_gemma":[0.9973111,0.00136684,0.0004149392,0.0004437786,0.0003935408,0.00006984031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007335394,0.0002037395,0.04098413,0.0004574046,0.0002281739,0.0002694642,0.000595873,0.5189037,0.03978842,0.01584849,0.00348615,0.378501],"study_design_scores_gemma":[0.00002243017,0.0002220037,0.01063517,0.00003587129,0.00002938406,0.0003520712,0.00006499163,0.9724464,0.01003496,0.003985277,0.002143096,0.00002835388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.263996,0.0002487311,0.7258952,0.0002612084,0.00006558741,0.0001980848,0.0004451708,0.002505941,0.006384095],"genre_scores_gemma":[0.8196383,0.0001118233,0.1782418,0.00004195126,0.00001258609,0.0001181074,0.0004044339,0.0004819604,0.0009491204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004668648,"threshold_uncertainty_score":0.0114333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06739895844597288,"score_gpt":0.3472506603618322,"score_spread":0.2798517019158593,"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."}}