{"id":"W2621393851","doi":"10.1017/cjn.2017.172","title":"P.088 Spinal computer-assisted intra-operative three-dimensional navigation in Canada: a population-based time trend study","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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada); Toronto Public Health","funders":"","keywords":"Medicine; Spinal deformity; Specialty; Logistic regression; Orthopedic surgery; Cohort; Population; Usability; Surgery; Scoliosis; Internal medicine; Family medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002391745,0.0003021484,0.0006093177,0.0007451319,0.002857445,0.0006316556,0.001209491,0.0001309966,0.0001684999],"category_scores_gemma":[0.001088818,0.0002040389,0.0001500907,0.0005719441,0.001691256,0.0007353668,0.00004945114,0.001120563,0.000001204835],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000930202,"about_ca_system_score_gemma":0.009620192,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5997842,"about_ca_topic_score_gemma":0.9846003,"domain_scores_codex":[0.9963222,0.0004748406,0.0009397237,0.0004861554,0.0009628714,0.0008142662],"domain_scores_gemma":[0.9968471,0.0002317231,0.00106485,0.0002149326,0.0004272909,0.00121409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002208772,0.00007006259,0.9828029,0.000004664081,0.00001146255,0.00711295,0.00005576896,0.002798315,0.00002100081,0.00003977551,0.0001727809,0.006689503],"study_design_scores_gemma":[0.0005697999,0.04618761,0.9336042,0.0001005813,0.00003097282,0.005437572,0.00005440644,0.01279797,0.00002868105,0.0009091051,0.0000859073,0.0001931517],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933105,0.0001626261,0.00003440455,0.005453081,0.000480487,0.0003194046,0.00001140419,0.00001225799,0.0002159096],"genre_scores_gemma":[0.9954157,0.0000065448,0.002080014,0.00224374,0.00023338,0.000003946513,0.000001774069,0.000009463821,0.000005444332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3848161,"threshold_uncertainty_score":0.9984407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03698760166483891,"score_gpt":0.3088130504198731,"score_spread":0.2718254487550342,"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."}}