{"id":"W2079086213","doi":"10.2214/ajr.08.1793","title":"Functional Joint Imaging Using 256-MDCT: Technical Feasibility","year":2009,"lang":"en","type":"article","venue":"American Journal of Roentgenology","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Johns Hopkins University","keywords":"Medicine; Joint (building); Radiology; Medical physics; Nuclear 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":[0.0003972747,0.0001214191,0.000472253,0.0001956043,0.0000668155,0.000008797106,0.0001286891,0.00003682284,0.0001582188],"category_scores_gemma":[0.0002087372,0.00009715974,0.0001930017,0.0002910352,0.000495587,0.00006153005,0.00003390308,0.000505542,0.000006688351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002176042,"about_ca_system_score_gemma":0.0002065253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000182048,"about_ca_topic_score_gemma":5.035413e-7,"domain_scores_codex":[0.9986234,0.00006090791,0.0005803593,0.0001849034,0.00028002,0.000270391],"domain_scores_gemma":[0.9987157,0.00003796021,0.000447862,0.0002816405,0.0002629792,0.0002538846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005542517,0.001348515,0.05312994,0.00001696914,0.0001117267,0.0003889817,0.00007728761,0.00004432367,0.7697911,0.002393215,0.01909472,0.153049],"study_design_scores_gemma":[0.004618772,0.007410171,0.8689669,0.0004065415,0.0008888005,0.06023145,0.000733236,0.00377204,0.0155773,0.01484468,0.02191255,0.0006374988],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8049241,0.0002060171,0.1459195,0.0480824,0.000112053,0.0001827817,0.000001931871,0.00007575177,0.0004954807],"genre_scores_gemma":[0.9106874,0.00005023801,0.08554248,0.003449491,0.0002327201,0.000001542615,0.000002547361,0.000009963589,0.0000235922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.815837,"threshold_uncertainty_score":0.3962059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.048142075446347,"score_gpt":0.3472185679626303,"score_spread":0.2990764925162833,"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."}}