{"id":"W3011066130","doi":"10.1007/s12195-020-00612-5","title":"Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy","year":2020,"lang":"en","type":"article","venue":"Cellular and Molecular Bioengineering","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Pohjois-Savon Rahasto; Saastamoisen säätiö; Päivikki ja Sakari Sohlbergin Säätiö; Killam Trusts; Canadian Institutes of Health Research; Itä-Suomen Yliopisto; Suomen Kulttuurirahasto; Kuopion Yliopistollinen Sairaala; Academy of Finland","keywords":"Cartilage; Artificial intelligence; Support vector machine; Kappa; Articular cartilage; Pattern recognition (psychology); Osteoarthritis; Machine learning; Anterior cruciate ligament; Computer science; Biomedical engineering; Mathematics; Pathology; Biology; Medicine; Radiology; Anatomy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006346859,0.0001508425,0.0002539959,0.00004835725,0.00004657404,0.00002085352,0.00003419006,0.00007911232,0.00001610676],"category_scores_gemma":[0.00005301223,0.0001498118,0.00008890624,0.0001512608,0.00003898264,0.0000435752,0.00003361367,0.0002050827,0.000003089885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002257834,"about_ca_system_score_gemma":0.00002915185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006257113,"about_ca_topic_score_gemma":2.323999e-7,"domain_scores_codex":[0.9992535,0.00002196844,0.0001936723,0.0002146369,0.0001394135,0.0001768381],"domain_scores_gemma":[0.9996042,0.000004900628,0.00004923837,0.0001292785,0.00003894392,0.0001734216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004001604,0.00002268327,0.0007317152,0.0001233536,0.00002426055,0.0001107827,0.0002355673,0.0001873542,0.9976581,0.000283602,0.000001164132,0.0005813696],"study_design_scores_gemma":[0.0009005137,0.0004934302,0.00006874395,0.00007196617,0.0001338007,0.00002040921,0.00008251626,0.1159378,0.8815756,0.00001386725,0.0005866806,0.0001146309],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.905175,0.00271787,0.09145448,0.000117841,0.00004065234,0.0002204933,0.000003713357,0.00007048128,0.0001994917],"genre_scores_gemma":[0.9856812,0.00004238852,0.01408318,0.00005818257,0.00003153826,0.00000398354,0.00005370001,0.00002988801,0.00001591749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1160825,"threshold_uncertainty_score":0.6109146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01778032986342964,"score_gpt":0.2331007765903941,"score_spread":0.2153204467269644,"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."}}