{"id":"W4328048685","doi":"10.2196/44666","title":"Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Silymarin and Mushroom Poisoning","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Triage; Medicine; Machine learning; Receiver operating characteristic; Mushroom poisoning; Cohort; Artificial intelligence; Gradient boosting; Emergency medicine; Poison control; Internal medicine; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219775,0.0002205028,0.0004894789,0.0008008958,0.0003139706,0.00005169984,0.000233377,0.0001407581,0.00010664],"category_scores_gemma":[0.002022207,0.0001588593,0.0001156375,0.001819214,0.000366096,0.0003331346,0.0002556778,0.001295019,0.0001836184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001213102,"about_ca_system_score_gemma":0.0001460585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001850669,"about_ca_topic_score_gemma":0.000005059557,"domain_scores_codex":[0.9963269,0.0003437998,0.0004656488,0.0003539133,0.001621213,0.0008885614],"domain_scores_gemma":[0.9956253,0.0005370911,0.0001068355,0.0003656337,0.003062333,0.0003028698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00757434,0.003307349,0.817542,0.003369477,0.0007463805,0.0001502754,0.08134833,0.00009707335,0.002120283,0.007889655,0.00680539,0.06904946],"study_design_scores_gemma":[0.01005742,0.005020924,0.9628773,0.0006961737,0.00003628867,0.0000141927,0.007191576,0.01056476,0.001182046,0.0001025166,0.001938522,0.0003183352],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688938,0.00003333467,0.0002863811,0.0004516872,0.00004711226,0.001047437,0.00002312899,0.00016732,0.02904985],"genre_scores_gemma":[0.9967172,0.00002646463,0.0007833815,0.0001156625,0.00006360188,0.0001818719,0.0002216745,0.00005686933,0.001833318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1453353,"threshold_uncertainty_score":0.6478096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04218603155495339,"score_gpt":0.3695565706262773,"score_spread":0.327370539071324,"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."}}