{"id":"W4385276248","doi":"10.1302/1358-992x.2023.8.040","title":"AUTOMATIC SEGMENTATION AND BONE ARCHITECTURE ANALYSIS OF SUBCHONDRAL BONE IN THE MURINE PROXIMAL TIBIA: EVALUATION OF AN OPEN-SOURCE PIPELINE","year":2023,"lang":"en","type":"article","venue":"Orthopaedic Proceedings","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Hospital","funders":"","keywords":"Segmentation; Pipeline (software); Subchondral bone; Tibia; Volume (thermodynamics); Biomedical engineering; X-ray microtomography; Trabecular bone; Computer science; Anatomy; Artificial intelligence; Medicine; Osteoporosis; Osteoarthritis; Pathology; Radiology; Physics","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":[0.001760175,0.0001529655,0.0004798863,0.0006981509,0.00005059205,0.00002919207,0.0001050314,0.00007342378,0.00005297572],"category_scores_gemma":[0.0002028052,0.0001104517,0.00007854768,0.001942329,0.00007130981,0.0002150759,0.00007401509,0.0001303073,0.000001610704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003782304,"about_ca_system_score_gemma":0.00007357886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004232275,"about_ca_topic_score_gemma":0.0001594768,"domain_scores_codex":[0.9981986,0.00004963077,0.0004736989,0.0002914674,0.0007883069,0.0001983028],"domain_scores_gemma":[0.9992415,0.00004195646,0.0002619411,0.0001472861,0.000236402,0.0000709024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007963156,0.001118591,0.1078326,0.0008114826,0.0003140055,0.00005542947,0.01741027,0.00008591005,0.3798462,0.000363604,0.0002560232,0.4911095],"study_design_scores_gemma":[0.04731384,0.01357188,0.5610716,0.001739534,0.02186574,0.0006092145,0.02235521,0.2544305,0.07389142,0.002015323,0.0002721928,0.0008635133],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973067,0.0002033277,0.0000203626,0.0005248663,0.00002559945,0.001521981,0.00001381905,0.00005322852,0.0003301327],"genre_scores_gemma":[0.9980328,0.0000291304,0.001182143,0.00007366762,0.00004495835,0.0001442858,0.0003089928,0.00001899776,0.0001650061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.490246,"threshold_uncertainty_score":0.450409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02441258129307246,"score_gpt":0.3069423099223011,"score_spread":0.2825297286292286,"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."}}