{"id":"W4387949947","doi":"10.1007/s11548-023-03029-3","title":"Image-guided surgical planning of percutaneous nephrolithotomy with patient-specific CTRs","year":2023,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Kidney Stones and Urolithiasis Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Association of Occupational Therapists; Western University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Percutaneous nephrolithotomy; Medicine; Percutaneous; Reachability; Fiducial marker; Computer science; Lithotomy position; Artificial intelligence; Surgery; Radiology; Algorithm","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":[0.0002496268,0.000490132,0.0003775055,0.0009051912,0.0003652423,0.0009586964,0.0004497174,0.0007015968,0.004348204],"category_scores_gemma":[0.001660749,0.0004687957,0.000740118,0.0005554227,0.0002792695,0.0006110554,0.0007361331,0.0006015389,0.0006905796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003869995,"about_ca_system_score_gemma":0.002495178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004651343,"about_ca_topic_score_gemma":0.006012922,"domain_scores_codex":[0.9997007,0.00007352926,0.00003654101,0.00004927655,0.00009797093,0.00004197446],"domain_scores_gemma":[0.9997016,0.0001244065,0.00003813459,0.00003619325,0.00005710595,0.00004261184],"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":[0.001843643,0.0002770578,0.06577061,0.001532824,0.000279437,0.01406615,0.0009669246,0.1994839,0.1565337,0.008237904,0.01613362,0.5348741],"study_design_scores_gemma":[0.0002716229,0.000974038,0.0609269,0.0003281122,0.0005024549,0.05301074,0.0005770929,0.6970886,0.1274651,0.009166569,0.04923385,0.0004549573],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.157614,0.0027441,0.8143305,0.0009104555,0.0001326886,0.0004554179,0.001305197,0.003293416,0.0192142],"genre_scores_gemma":[0.6511853,0.001132482,0.3438817,0.0002474466,0.00008480793,0.0002756105,0.0006415038,0.0003100355,0.002241055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004651343,"threshold_uncertainty_score":0.01454616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02640520370091952,"score_gpt":0.2928757680072326,"score_spread":0.2664705643063131,"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."}}