{"id":"W4379524175","doi":"10.1136/annrheumdis-2023-eular.2251","title":"POS0920 QUANTIFICATION OF TENOSYNOVITIS IN RA FROM WRIST MRIs, BASED ON DEEP LEARNING","year":2023,"lang":"en","type":"article","venue":"Annals of the Rheumatic Diseases","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Arthritis Society; Gilead Sciences; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Dutch Arthritis Society; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb","keywords":"Medicine; Wrist; Tenosynovitis; Physical medicine and rehabilitation; Physical therapy; Artificial intelligence; Radiology; Surgery; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004500729,0.0006434221,0.0005117091,0.001108068,0.0001757975,0.0006840925,0.0003942699,0.0006845999,0.002188483],"category_scores_gemma":[0.001100888,0.0002679619,0.0003503102,0.0004917774,0.000162899,0.0003660755,0.0004546991,0.0003563892,0.0008746156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001969909,"about_ca_system_score_gemma":0.0004690053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003103535,"about_ca_topic_score_gemma":0.006297864,"domain_scores_codex":[0.9998584,0.00002635707,0.00001401301,0.0000306697,0.00004644972,0.00002405963],"domain_scores_gemma":[0.9997626,0.00006780264,0.0000364726,0.00001813712,0.0000873723,0.00002760178],"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":[0.002497953,0.0004489676,0.03377381,0.001060298,0.0005183428,0.0007872295,0.0001060926,0.1115334,0.1453006,0.001732028,0.01559853,0.6866428],"study_design_scores_gemma":[0.00006903952,0.0003598853,0.02125232,0.0001028962,0.0001541399,0.0008617878,0.00004217452,0.9317808,0.03957066,0.00137421,0.004367651,0.00006448447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6254845,0.007736632,0.3420829,0.0006827202,0.0004228204,0.0002229887,0.005833838,0.006631479,0.01090207],"genre_scores_gemma":[0.8961933,0.001330518,0.0893733,0.0002972283,0.0001595703,0.0001139309,0.004322822,0.0002402661,0.007969006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003103535,"threshold_uncertainty_score":0.007321179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03028411556907106,"score_gpt":0.3261604761007173,"score_spread":0.2958763605316462,"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."}}