{"id":"W3158366443","doi":"10.1177/03635465211010798","title":"Validation of a Risk Calculator to Personalize Graft Choice and Reduce Rupture Rates for Anterior Cruciate Ligament Reconstruction","year":2021,"lang":"en","type":"article","venue":"The American Journal of Sports Medicine","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact; Fowler Kennedy Sport Medicine Clinic; McMaster University; London Health Sciences Centre; Lawson Health Research Institute; Western University","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; International Society of Arthroscopy, Knee Surgery and Orthopaedic Sports Medicine","keywords":"Calculator; Anterior cruciate ligament reconstruction; Medicine; Anterior cruciate ligament; Discriminative model; Sports medicine; Randomized controlled trial; Orthopedic surgery; Surgery; Physical medicine and rehabilitation; Physical therapy; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06106414,0.0009875995,0.0008842655,0.001196929,0.0004977667,0.001544453,0.001675209,0.0011882,0.001997422],"category_scores_gemma":[0.1746006,0.0003953017,0.001849001,0.0008443415,0.0008768882,0.0009056006,0.001552757,0.00161673,0.000457981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005937342,"about_ca_system_score_gemma":0.001950322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006744318,"about_ca_topic_score_gemma":0.0008514848,"domain_scores_codex":[0.966684,0.0263329,0.00207039,0.001786475,0.002782268,0.0003440029],"domain_scores_gemma":[0.8572624,0.09350164,0.02638286,0.01297475,0.008684741,0.001193652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.03820851,0.004603402,0.8406333,0.00089976,0.003334737,0.00006360542,0.0004769588,0.009264854,0.0007361894,0.001145005,0.004578536,0.09605496],"study_design_scores_gemma":[0.03348905,0.07462728,0.7069439,0.001984635,0.009868474,0.0009706105,0.0006188353,0.1423676,0.009404838,0.005309653,0.01411842,0.0002967172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974299,0.001265609,0.01511093,0.0006357754,0.0001295435,0.003089229,0.002818623,0.0001586771,0.002492667],"genre_scores_gemma":[0.9802632,0.0002490657,0.01432764,0.0002473679,0.00006681728,0.001864397,0.002658643,0.00003184214,0.0002910715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06106414,"threshold_uncertainty_score":0.3229419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01051742719151397,"score_gpt":0.3174515091487695,"score_spread":0.3069340819572555,"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."}}