{"id":"W3154888856","doi":"10.1177/09622802211003620","title":"Calibration of surgical tools using multilevel modeling with LINEX loss function: Theory and experiment","year":2021,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Aga Khan Foundation","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exponential function; Bayesian probability; Computer science; Calibration; Function (biology); Mean squared error; Likelihood function; Linear model; Multilevel model; Statistics; Mathematics; Algorithm; Artificial intelligence; Machine learning; Estimation theory","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008616571,0.000113508,0.0004204843,0.0001400142,0.00007890482,0.00002835193,0.00005540228,0.0001865365,0.003878019],"category_scores_gemma":[0.01437805,0.00008223785,0.00003262971,0.0004429034,0.0005691193,0.00006611498,0.0001114604,0.0008091879,9.937127e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006108687,"about_ca_system_score_gemma":0.000560103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000254825,"about_ca_topic_score_gemma":0.000003117614,"domain_scores_codex":[0.9942781,0.0029141,0.0005165404,0.0003888343,0.00153594,0.0003664929],"domain_scores_gemma":[0.9834494,0.01546834,0.00002919521,0.0001810072,0.0003744055,0.0004976438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004135782,0.0004498105,0.001243252,0.0002321771,0.00007249648,0.001932348,0.0004310817,0.0008157821,0.001622926,0.2991079,0.000004219703,0.6899522],"study_design_scores_gemma":[0.004460108,0.0003512517,0.000597256,0.0004175142,0.00002609748,0.0001198102,0.0008846613,0.9730963,0.001793023,0.01775992,0.0003879789,0.0001060541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08856938,0.0003556171,0.9089128,0.0004149827,0.00004735778,0.0001865112,0.000008347581,0.00001370573,0.00149124],"genre_scores_gemma":[0.6673646,0.00006935214,0.3323013,0.00008515781,0.00008929749,0.00001439956,0.000018506,0.00001431265,0.00004312359],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9722806,"threshold_uncertainty_score":0.9970326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3492482787356758,"score_gpt":0.5901490617878591,"score_spread":0.2409007830521833,"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."}}