{"id":"W2886546589","doi":"10.1136/annrheumdis-2018-eular.6880","title":"FRI0597 Validation of web-based calibration modules for imaging scoring systems based on principles of artificial intelligence: the sparcc mri sacroiliac joint inflammation score","year":2018,"lang":"en","type":"article","venue":"Annals of the Rheumatic Diseases","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; McMaster Children's Hospital; University of Alberta","funders":"","keywords":"Medicine; Medical physics; DICOM; Artificial intelligence; Quadrant (abdomen); Calibration; Computer science; Machine learning; Physical therapy; Radiology; Pathology; Statistics","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":[0.0008971728,0.0001390215,0.0003509406,0.0001464868,0.0001352558,0.00003471135,0.0001734472,0.00003194646,0.00001821129],"category_scores_gemma":[0.002656987,0.00008383705,0.0002093146,0.0002028729,0.0003547006,0.00009682602,0.00003379165,0.00008960113,0.000001004123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001954956,"about_ca_system_score_gemma":0.0002001382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007913632,"about_ca_topic_score_gemma":0.000002329271,"domain_scores_codex":[0.9981739,0.0001677818,0.0008054472,0.0001707534,0.0005160214,0.0001661087],"domain_scores_gemma":[0.997824,0.0004960166,0.0007603942,0.0004662568,0.0003857695,0.00006761352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001836885,0.001174793,0.04280677,0.01354143,0.0004156601,0.000002053972,0.00155015,0.7157467,0.03249433,0.03222881,0.001556039,0.1566464],"study_design_scores_gemma":[0.0002426279,0.0001401769,0.006922883,0.00428729,0.0001170402,7.970816e-7,0.000170038,0.8626482,0.1235473,0.001824097,0.00003010241,0.00006935759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8754584,0.0001987201,0.1177982,0.005089443,0.0003140481,0.0009776596,0.0000459462,0.00002862579,0.00008886726],"genre_scores_gemma":[0.9984179,0.0000276241,0.001170444,0.0001289676,0.0001377501,0.00004641688,0.000040716,0.00002017306,0.00001005326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.156577,"threshold_uncertainty_score":0.3418775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06798254035158761,"score_gpt":0.3275498075948006,"score_spread":0.259567267243213,"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."}}