{"id":"W2002529663","doi":"10.1007/s11999-013-2878-x","title":"Can Fluoroscopy-based Computer Navigation Improve Entry Point Selection for Intramedullary Nailing of Femur Fractures?","year":2013,"lang":"en","type":"article","venue":"Clinical Orthopaedics and Related Research","topic":"Hip and Femur Fractures","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; St. Michael's Hospital","funders":"","keywords":"Fluoroscopy; Medicine; Femur; Intramedullary rod; Greater trochanter; Navigation system; Reproducibility; Cadaveric spasm; Computer vision; Artificial intelligence; Radiology; Nuclear medicine; Surgery; Computer science; Mathematics","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":[0.003123796,0.0003338569,0.0003423747,0.0009762592,0.0001488796,0.0005772912,0.0005640712,0.0006175175,0.00106907],"category_scores_gemma":[0.02933798,0.0002646907,0.0003029012,0.0006174892,0.0006519392,0.0007701343,0.0003449709,0.0002474286,0.0003130161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003996289,"about_ca_system_score_gemma":0.0007242096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002857526,"about_ca_topic_score_gemma":0.005324644,"domain_scores_codex":[0.9984859,0.0006038857,0.0001384501,0.0002030348,0.0005040719,0.00006470656],"domain_scores_gemma":[0.9901478,0.005494107,0.002012052,0.000608156,0.001604159,0.0001337415],"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":[0.001972577,0.0002808929,0.2216216,0.0005251237,0.0001058179,0.0001631352,0.0007708552,0.01034761,0.03188419,0.0002317789,0.0006880262,0.7314083],"study_design_scores_gemma":[0.0003733535,0.006541732,0.8187104,0.0006518099,0.0005876651,0.006637241,0.0006600236,0.07655425,0.08023407,0.001714767,0.006926656,0.0004080141],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8965239,0.003394128,0.09726865,0.0004282757,0.00006562874,0.00009247821,0.00009982653,0.0007140955,0.001413034],"genre_scores_gemma":[0.9246516,0.000973406,0.07390573,0.00008769239,0.00003898342,0.00003312016,0.00008165858,0.00006227775,0.0001655967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003123796,"threshold_uncertainty_score":0.01652038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0360616487657644,"score_gpt":0.4114574149073349,"score_spread":0.3753957661415705,"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."}}