{"id":"W4389397788","doi":"10.2196/53058","title":"Risk Prediction of Emergency Department Visits in Patients With Lung Cancer Using Machine Learning: Retrospective Observational Study","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Emergency department; Observational study; Lung cancer; Medicine; Retrospective cohort study; Emergency medicine; Medical emergency; Machine learning; Computer science; Medical physics; Artificial intelligence; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002010117,0.0003526934,0.0006380074,0.0009053193,0.000489743,0.0007540358,0.0005572633,0.0005837262,0.0006208133],"category_scores_gemma":[0.006161223,0.0004183211,0.001140835,0.001074527,0.0002764569,0.0007225554,0.0007193465,0.001183252,0.000139722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005041437,"about_ca_system_score_gemma":0.000719169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003583195,"about_ca_topic_score_gemma":0.003243806,"domain_scores_codex":[0.9984615,0.0004782031,0.0002891257,0.0003089358,0.0002836671,0.0001786323],"domain_scores_gemma":[0.9958239,0.001406479,0.001446564,0.0004596692,0.0004723709,0.0003909998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006927994,0.0001006596,0.9988889,0.000008257895,0.00004491575,0.00005146489,0.00003065323,0.0001197936,0.00003343338,0.00001039009,0.00004580412,0.0005964634],"study_design_scores_gemma":[0.00003466001,0.0006879444,0.9882122,0.00002759265,0.000131413,0.0006396002,0.0005362378,0.009183923,0.000143274,0.00008332561,0.0003023193,0.00001753137],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990403,0.0001150226,0.0004406605,0.00003422783,0.000005292346,0.00003358534,0.0002156063,0.000003213725,0.000112245],"genre_scores_gemma":[0.9991776,0.00006694687,0.000279072,0.00001851586,0.000006090454,0.00002621672,0.000389145,0.00000125235,0.00003515305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003583195,"threshold_uncertainty_score":0.01063067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07593051275939339,"score_gpt":0.3799649813524105,"score_spread":0.3040344685930171,"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."}}