{"id":"W4410432326","doi":"10.1007/s11548-025-03395-0","title":"Virtual fluoroscopy for interventional guidance using magnetic tracking","year":2025,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Northern Digital (Canada); Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Fluoroscopy; Tracking (education); Medical physics; Computer science; Health informatics; Radiology; Interventional radiology; Medicine; Psychology; Pathology; Public health","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004717397,0.0005023101,0.0003915243,0.0006865007,0.000373135,0.001466301,0.0006437979,0.001036331,0.003333385],"category_scores_gemma":[0.001901909,0.0004763708,0.0006332353,0.0004503115,0.0003253936,0.0009240902,0.001160438,0.0005745074,0.0009567478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002333674,"about_ca_system_score_gemma":0.0007426038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009042525,"about_ca_topic_score_gemma":0.0008028774,"domain_scores_codex":[0.9995527,0.00014159,0.00003347682,0.00007001611,0.0001529974,0.00004926353],"domain_scores_gemma":[0.9993455,0.0003034607,0.00007225221,0.0001213761,0.0001021376,0.00005536041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001936123,0.000208557,0.002868068,0.000622045,0.000147099,0.001241111,0.0005055218,0.02476146,0.4244116,0.007406719,0.005590461,0.5303013],"study_design_scores_gemma":[0.000356975,0.002668885,0.01699473,0.0005049839,0.0007550918,0.02258698,0.0003028709,0.4805029,0.3831983,0.009913174,0.08154774,0.0006673555],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06601059,0.002927827,0.9203444,0.0001981589,0.0002489099,0.00008769499,0.0001492294,0.001817939,0.008215269],"genre_scores_gemma":[0.7297698,0.001688195,0.2643432,0.0002198858,0.00008502727,0.0000894849,0.0001645717,0.0002132538,0.003426635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003333385,"threshold_uncertainty_score":0.01115131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03222316505109914,"score_gpt":0.3221863714839944,"score_spread":0.2899632064328952,"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."}}