{"id":"W4402133224","doi":"10.1093/dote/doae057.345","title":"644. CUSTOMIZING A LARGE LANGUAGE MODEL TO PROVIDE CLINICALLY TAILORED ADVICE FOR EMERGENCY ESOPHAGEAL IMPACTIONS","year":2024,"lang":"en","type":"article","venue":"Diseases of the Esophagus","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Medicine; Advice (programming); Medical emergency; Emergency department; Medical physics; General surgery; Nursing; Programming language","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":[0.002938206,0.0008850493,0.0003345997,0.0008887969,0.0002922896,0.001536325,0.0009575197,0.0008622857,0.01195419],"category_scores_gemma":[0.01422775,0.000359996,0.001003547,0.0003732063,0.0003145081,0.001423062,0.001202886,0.0005378764,0.004707465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007992397,"about_ca_system_score_gemma":0.001465938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004487888,"about_ca_topic_score_gemma":0.004563951,"domain_scores_codex":[0.9984497,0.0007104819,0.0002454494,0.0002447911,0.0002559428,0.00009364334],"domain_scores_gemma":[0.9930496,0.004892916,0.0004563825,0.0005722741,0.0007860835,0.0002427607],"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":[0.003755293,0.001923761,0.1004961,0.002150041,0.0002243024,0.002947677,0.00414358,0.04231427,0.02858802,0.0037018,0.1004095,0.7093456],"study_design_scores_gemma":[0.001645132,0.002965531,0.04938619,0.001566645,0.0007813171,0.005907388,0.003262945,0.6147559,0.04538051,0.01206274,0.2616044,0.00068138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4820791,0.0007499266,0.3217992,0.006451564,0.0004798397,0.00469668,0.01378849,0.1276463,0.0423088],"genre_scores_gemma":[0.67485,0.0004006785,0.2988886,0.001490236,0.0001009615,0.001137738,0.01040248,0.002024494,0.01070475],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01195419,"threshold_uncertainty_score":0.03999072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769013709158172,"score_gpt":0.3571918074988752,"score_spread":0.3395016704072935,"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."}}