{"id":"W2805478493","doi":"10.1007/s11517-018-1853-9","title":"Dynamic ensemble selection of learner-descriptor classifiers to assess curve types in adolescent idiopathic scoliosis","year":2018,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies; Consejo Nacional de Ciencia y Tecnología; Polytechnique Montréal","keywords":"Artificial intelligence; Reliability (semiconductor); Ensemble learning; Pattern recognition (psychology); Computer science; Scoliosis; Curvature; Idiopathic scoliosis; Selection (genetic algorithm); Machine learning; Mathematics; Medicine","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":[0.0006793951,0.0002132168,0.0005415026,0.0002257059,0.00004846525,0.00001421672,0.0001384445,0.0002635923,0.00007843415],"category_scores_gemma":[0.001295979,0.0001597121,0.0001094469,0.0006822621,0.00008388831,0.00002360432,0.0001119369,0.0003919407,0.00004694807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002630688,"about_ca_system_score_gemma":0.00009114377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005799238,"about_ca_topic_score_gemma":0.00002055533,"domain_scores_codex":[0.9981895,0.00007984359,0.0004941167,0.0004101007,0.0003793482,0.0004470772],"domain_scores_gemma":[0.9992352,0.00009471155,0.00007324001,0.0001496399,0.0001074361,0.0003397604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002057902,0.001313115,0.9348627,0.0003318168,0.0001252973,0.00008104421,0.0002527831,0.00162084,0.03365028,0.0003701669,0.0001510486,0.02703517],"study_design_scores_gemma":[0.0009178608,0.001408745,0.8822621,0.003485919,0.00003406484,0.00003820643,0.00005005597,0.1058661,0.005431205,0.000007686057,0.0002904228,0.0002076513],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854895,0.0005292399,0.0125282,0.0005179324,0.0003758887,0.0003227968,7.503357e-7,0.0001104201,0.0001252969],"genre_scores_gemma":[0.9973077,0.0000515359,0.001935192,0.0004487046,0.0002089539,0.00001984808,0.000003035484,0.00001959392,0.000005463968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1042453,"threshold_uncertainty_score":0.6512871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0601347657463528,"score_gpt":0.3222208682156557,"score_spread":0.2620861024693029,"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."}}