{"id":"W4206029591","doi":"10.1136/tsaco-2021-000821","title":"A core outcome set for damage control laparotomy via modified Delphi method","year":2022,"lang":"en","type":"article","venue":"Trauma Surgery & Acute Care Open","topic":"Trauma and Emergency Care Studies","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Center for Advancing Translational Sciences; National Institute of General Medical Sciences; Agency for Healthcare Research and Quality; National Institutes of Health; Patient-Centered Outcomes Research Institute; Henry M. Jackson Foundation; National Heart, Lung, and Blood Institute; U.S. Department of Defense","keywords":"Outcome (game theory); Core (optical fiber); Set (abstract data type); Laparotomy; Delphi method; Control (management); Computer science; Medicine; Artificial intelligence; Mathematics; Surgery","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001095349,0.0004477137,0.001573434,0.0002355113,0.0007497977,0.0000603372,0.0005777539,0.0001052273,0.0008227017],"category_scores_gemma":[0.0001399747,0.0004104174,0.0007855106,0.000468306,0.00007244964,0.0001952641,0.0004093319,0.0004058788,0.00001867274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002547571,"about_ca_system_score_gemma":0.0002100583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007359173,"about_ca_topic_score_gemma":0.00009789845,"domain_scores_codex":[0.9969667,0.0002128862,0.0008756334,0.0006558566,0.0005440274,0.0007448511],"domain_scores_gemma":[0.9976184,0.0007156176,0.0002351651,0.0007574327,0.0004043693,0.0002689797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01580448,0.0008819439,0.143684,0.001461691,0.008421533,0.00207458,0.03430855,0.0006859623,0.004871062,0.001102582,0.5262072,0.2604964],"study_design_scores_gemma":[0.0302826,0.002482878,0.1431674,0.0002174484,0.007518839,0.001170714,0.07344215,0.002773335,0.002332035,0.0007689031,0.7323141,0.003529622],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9243728,0.001930882,0.008885564,0.005082188,0.004244518,0.01365818,0.007363447,0.0005491729,0.03391323],"genre_scores_gemma":[0.987551,0.0000288531,0.002033214,0.002931255,0.0002118083,0.004068135,0.00126499,0.0001122083,0.001798591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2569668,"threshold_uncertainty_score":0.9998348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2431699761354577,"score_gpt":0.4400522032812749,"score_spread":0.1968822271458172,"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."}}