{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001445405,0.0005783643,0.0004987571,0.001129992,0.00013731,0.0007172921,0.0004705144,0.0008040476,0.0007146547],"category_scores_gemma":[0.002502901,0.0001492114,0.0004756273,0.0005551205,0.000306164,0.0005588178,0.0002790191,0.0004906501,0.0004133261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003526522,"about_ca_system_score_gemma":0.0003005052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001260308,"about_ca_topic_score_gemma":0.0009240439,"domain_scores_codex":[0.9994386,0.0001817009,0.00004207598,0.000148189,0.0001357628,0.00005361043],"domain_scores_gemma":[0.998726,0.0005998255,0.0002478692,0.00008877668,0.0002945522,0.000042954],"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":[0.0005380231,0.0007867603,0.0787596,0.0004173319,0.0004418585,0.0002329175,0.0001315714,0.2691008,0.1121478,0.001186621,0.002832331,0.5334244],"study_design_scores_gemma":[0.00000635151,0.0001253991,0.01419408,0.00002448483,0.00002574131,0.00007483983,0.00002918125,0.9741687,0.01016378,0.0008471729,0.0003241133,0.00001621578],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6715721,0.002574071,0.3209845,0.0005074511,0.0001284454,0.0000768955,0.0004834124,0.001044214,0.002628898],"genre_scores_gemma":[0.9606715,0.0003471812,0.03737834,0.00007184632,0.00004434981,0.00004237644,0.0003403132,0.00001133468,0.001092738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001445405,"threshold_uncertainty_score":0.007644057,"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."}}