{"id":"W1978705241","doi":"10.1016/j.engappai.2005.03.006","title":"Prediction of anterior scoliotic spinal curve from trunk surface using support vector regression","year":2005,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Generalizability theory; Principal component analysis; Regression; Functional principal component analysis; Artificial intelligence; Trunk; Dimensionality reduction; Scoliosis; Pattern recognition (psychology); Machine learning; Statistics; Mathematics","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":[0.0001612533,0.00014871,0.0002644806,0.0001203127,0.00003651475,0.00001543545,0.0002101693,0.00007327498,0.0001160011],"category_scores_gemma":[0.00005100852,0.0001546399,0.00009144843,0.0004966332,0.00006467648,0.0001187915,0.00002426635,0.0001576225,0.00002451366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000057458,"about_ca_system_score_gemma":0.00002324705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008125362,"about_ca_topic_score_gemma":0.000003382825,"domain_scores_codex":[0.998787,0.00001024215,0.0006124449,0.0001912078,0.0002174127,0.0001817334],"domain_scores_gemma":[0.9993596,0.00005277808,0.00009203573,0.0003210107,0.0000824995,0.00009207243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006167911,0.00006105719,0.0002272972,0.00009786736,0.00004721714,4.912667e-7,0.0001613129,0.579908,0.3867251,0.0003753523,0.00002514669,0.03236504],"study_design_scores_gemma":[0.0000145379,0.00001721264,0.0002614177,0.0001778534,0.00003931929,0.000001316641,0.00004336259,0.6976665,0.3014097,0.00007891078,0.0002024955,0.00008739508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.388929,0.0003707531,0.6103041,0.0000239336,0.00009117793,0.00009778621,0.00004321413,0.0001161957,0.00002376956],"genre_scores_gemma":[0.9683329,0.00007142193,0.03132973,0.000002831844,0.0001983128,0.00001237338,0.00001918648,0.00002474145,0.000008531683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5794038,"threshold_uncertainty_score":0.6306032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0318299764262024,"score_gpt":0.2807116068028466,"score_spread":0.2488816303766442,"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."}}