{"id":"W2222318341","doi":"10.1016/j.compmedimag.2015.12.006","title":"A multi-center milestone study of clinical vertebral CT segmentation","year":2016,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; University of British Columbia","funders":"National Institutes of Health","keywords":"Segmentation; Vertebra; Sørensen–Dice coefficient; Milestone; Medicine; Thoracic vertebrae; Computer science; Artificial intelligence; Image segmentation; Lumbar; Lumbar vertebrae; Radiology; Anatomy; Cartography","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":[0.00119043,0.0003157068,0.0004079817,0.002608988,0.0005101023,0.0008362633,0.0006482086,0.0009524091,0.001790861],"category_scores_gemma":[0.004845984,0.0004075413,0.0003345406,0.001293426,0.0003928577,0.0008226893,0.000767148,0.0004198408,0.0006481477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007377614,"about_ca_system_score_gemma":0.0004496904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005464217,"about_ca_topic_score_gemma":0.007611659,"domain_scores_codex":[0.9994119,0.0001365784,0.00006444778,0.0001806893,0.0001166377,0.00008973067],"domain_scores_gemma":[0.9965719,0.001127882,0.0003045257,0.0005022304,0.001181139,0.0003124358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007899567,0.002601647,0.7532223,0.0002782308,0.0004411486,0.00842508,0.002252828,0.01008029,0.07307161,0.0007811508,0.002132396,0.1388137],"study_design_scores_gemma":[0.0001198187,0.001698441,0.9387655,0.00003292719,0.000207675,0.01416651,0.001454092,0.02310156,0.01776535,0.0003203391,0.002264775,0.0001028934],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966336,0.0001478739,0.002043134,0.00003709309,0.000004807467,0.00004273096,0.0003964181,0.00004459635,0.0006497921],"genre_scores_gemma":[0.9974774,0.00006495029,0.001496095,0.0000156757,0.000007647043,0.00001217676,0.0005445503,0.00004812677,0.0003335306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005464217,"threshold_uncertainty_score":0.01086485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02823088218053465,"score_gpt":0.3265334777514597,"score_spread":0.298302595570925,"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."}}