{"id":"W4205740553","doi":"10.1017/cjn.2021.445","title":"P.169 Reduced radiation CT imaging for augmented reality spinal surgery applications","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Medicine; Image quality; Augmented reality; Artificial intelligence; Image noise; Computer vision; Noise (video); Radiation exposure; Noise reduction; Radiology; Computer science; Nuclear medicine; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00366925,0.0002410355,0.0004644641,0.0006839124,0.001778961,0.0006333088,0.0008848681,0.00006479908,0.00009611862],"category_scores_gemma":[0.002250002,0.0001867861,0.0003376958,0.001507745,0.002127149,0.0006656499,0.00002412706,0.0006931982,0.000001939997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002239374,"about_ca_system_score_gemma":0.002331741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000355786,"about_ca_topic_score_gemma":0.004598584,"domain_scores_codex":[0.9967138,0.0004477212,0.0008695268,0.000402196,0.0005371047,0.001029647],"domain_scores_gemma":[0.9968522,0.0006566466,0.0003697951,0.0001457375,0.0004515094,0.001524068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003179949,0.00006838666,0.7551335,0.00007608208,0.00009369439,0.006568635,0.0001835109,0.03570886,0.0009263858,0.001182987,0.01711291,0.1829132],"study_design_scores_gemma":[0.001801055,0.01568351,0.3258347,0.0004727621,0.000723731,0.1146905,0.001450237,0.2457699,0.006914682,0.08476246,0.1989335,0.002962944],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839876,0.003419676,0.003213515,0.007303181,0.0009259948,0.0001178315,0.00002019162,0.00004567659,0.0009663281],"genre_scores_gemma":[0.9956272,0.0006697979,0.001668304,0.001557458,0.0004373894,0.000009296657,0.000002164259,0.00001214712,0.00001628119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4292989,"threshold_uncertainty_score":0.9995206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03867204163035492,"score_gpt":0.2857215666049391,"score_spread":0.2470495249745842,"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."}}