{"id":"W2508374236","doi":"10.1109/memea.2016.7533731","title":"Recognition of vertebral compression fractures in magnetic resonance images using statistics of height and width","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Sagittal plane; Naive Bayes classifier; Lumbar; Magnetic resonance imaging; Pattern recognition (psychology); Receiver operating characteristic; Vertebral compression fracture; Artificial intelligence; Contextual image classification; Osteoporosis; Computer science; Feature selection; Radiology; Medicine; Support vector machine; Pathology; Machine learning","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":[0.00004833913,0.00004532873,0.000117674,0.00005475939,0.000005742272,0.000002508697,0.00002490656,0.00002169299,0.0002178062],"category_scores_gemma":[0.00004549756,0.00002884274,0.000009571051,0.00005519585,0.00005072365,0.00004493946,0.000009195528,0.0000322394,8.054377e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005446498,"about_ca_system_score_gemma":0.000003384485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005913727,"about_ca_topic_score_gemma":0.000007369453,"domain_scores_codex":[0.9996364,0.00001533546,0.0001479721,0.00005693836,0.00007726858,0.00006606231],"domain_scores_gemma":[0.9998071,0.00007635757,0.00001907739,0.00005296683,0.00001993286,0.00002459463],"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":[0.000005752692,0.00002350561,0.01860244,0.0001446318,0.000007613643,0.000004164819,0.00007024138,0.0001070816,0.1668831,0.00003095236,0.00103456,0.813086],"study_design_scores_gemma":[0.001869047,0.00007449837,0.2078829,0.002196454,0.00008446351,0.000005547704,0.0000633366,0.3750873,0.4026288,0.009357017,0.0004145406,0.0003360481],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8893754,0.002161191,0.1079036,0.00003471613,0.00003189815,0.00002947,0.00005204454,0.00001960534,0.0003919768],"genre_scores_gemma":[0.9737763,0.0003160383,0.02584852,0.000009431139,0.00000816511,4.351838e-7,0.000002685374,0.000004940316,0.00003343726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8127499,"threshold_uncertainty_score":0.2384825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01051645747969651,"score_gpt":0.2372736371005025,"score_spread":0.226757179620806,"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."}}