{"id":"W2089882725","doi":"10.1007/s11548-009-0289-y","title":"Landmarking and segmentation of computed tomographic images of pediatric patients with neuroblastoma","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Health Research Board","keywords":"Computed tomographic; Computer science; Segmentation; Artificial intelligence; Computer vision; Neuroblastoma; Radiology; Computed tomography; Medical physics; Computer graphics (images); Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001611496,0.00006821401,0.0002552392,0.0004004658,0.00001169348,0.00001135867,0.00006335835,0.000031291,0.000002545021],"category_scores_gemma":[0.00001448473,0.00005212954,0.0000578066,0.0001053166,0.00005104428,0.00008616704,0.000008078309,0.00009467539,3.319621e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005246561,"about_ca_system_score_gemma":0.0000103383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.598039e-7,"about_ca_topic_score_gemma":1.024991e-7,"domain_scores_codex":[0.9993183,0.0000520506,0.0003561389,0.00005713853,0.0001550748,0.00006129125],"domain_scores_gemma":[0.9992521,0.0002787178,0.0002071323,0.00003100169,0.0001881229,0.00004292126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005671861,0.00007309075,0.9227085,0.00002975843,0.0003811521,0.00003597601,0.00007015077,0.002128923,0.000589903,0.000008709954,0.0006203735,0.07329675],"study_design_scores_gemma":[0.0005250698,0.0000874692,0.9903384,0.00006628062,0.00006328573,0.0001178482,0.000003991454,0.008515303,0.0001774991,0.00003505513,0.00001390232,0.00005591212],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622961,0.0003843857,0.03690384,0.0001111391,0.0002697734,0.00001425961,0.000003354079,0.00000787487,0.000009296286],"genre_scores_gemma":[0.9971492,0.0002162891,0.002432644,0.00005657739,0.0001341027,1.217092e-7,0.000007169818,0.000003297908,5.837983e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07324084,"threshold_uncertainty_score":0.2125781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004820861200798918,"score_gpt":0.1999963669817344,"score_spread":0.1951755057809355,"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."}}