{"id":"W2032234501","doi":"10.1002/jmri.20651","title":"Diffusion tensor imaging in evaluation of human skeletal muscle injury","year":2006,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":213,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; St. Joseph’s Healthcare Hamilton","funders":"Johns Hopkins University","keywords":"Diffusion MRI; Fractional anisotropy; Effective diffusion coefficient; Skeletal muscle; Magnetic resonance imaging; Medicine; Nuclear magnetic resonance; Anatomy; Nuclear medicine; Biomedical engineering; Physics; Radiology","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":[0.0008028166,0.0001282874,0.0002904066,0.0003232669,0.00005333167,0.00001503985,0.000134208,0.00002147641,0.00005958373],"category_scores_gemma":[0.0001290054,0.0001154501,0.0001113413,0.0002876697,0.0001161788,0.0001817856,0.00004254888,0.0002768215,0.000001169049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000120421,"about_ca_system_score_gemma":0.00008270898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006029022,"about_ca_topic_score_gemma":0.00000309261,"domain_scores_codex":[0.9981738,0.00006858868,0.0007518077,0.0001785193,0.0006237481,0.0002035339],"domain_scores_gemma":[0.9987373,0.00004321789,0.0004380863,0.0002559244,0.0004767932,0.0000486798],"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":[0.00002757123,0.0002617314,0.2729645,0.00002415798,7.55328e-7,0.00004297795,0.00004444555,0.00002982611,0.2467483,0.0001719007,0.0009279206,0.4787559],"study_design_scores_gemma":[0.001701396,0.0001341657,0.9744532,0.0004697812,0.00007570485,0.0002097409,0.00006721354,0.007679986,0.003122594,0.004917125,0.007056416,0.0001126765],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875631,0.007791277,0.0009549005,0.001672801,0.00005026882,0.0003216996,0.000003175358,0.0000231105,0.001619658],"genre_scores_gemma":[0.9914811,0.0001222021,0.007998147,0.0001261483,0.0001509164,0.00001150194,0.00000271135,0.0000238153,0.00008349626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7014887,"threshold_uncertainty_score":0.470792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03270774088411862,"score_gpt":0.3580757512026797,"score_spread":0.3253680103185611,"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."}}