{"id":"W3185975388","doi":"10.2196/27110","title":"Prediction Model of Anastomotic Leakage Among Esophageal Cancer Patients After Receiving an Esophagectomy: Machine Learning Approach","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Esophagectomy; Logistic regression; Random forest; Framingham Risk Score; Decision tree; Esophageal cancer; Population; Internal medicine; Surgery; Machine learning; Cancer; Disease; 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":[0.000358449,0.0002541543,0.000511725,0.0001740827,0.00010571,0.00003516734,0.0001517835,0.0002398846,0.0005647222],"category_scores_gemma":[0.0002134096,0.0002058077,0.0001437747,0.0004001823,0.0001959327,0.0005099164,0.0001528741,0.0008661074,0.00001025751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002664108,"about_ca_system_score_gemma":0.0006375907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006172564,"about_ca_topic_score_gemma":0.00004736251,"domain_scores_codex":[0.9967113,0.0001050296,0.0007976546,0.0002292892,0.00166587,0.0004909294],"domain_scores_gemma":[0.9982377,0.0000471962,0.0002109356,0.0003523648,0.0003473251,0.0008045218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004358735,0.002190586,0.9583171,0.002147741,0.0003049919,0.000218596,0.009227487,0.002058617,0.00001671358,0.00004209263,0.0001855973,0.02485459],"study_design_scores_gemma":[0.004471886,0.0009925436,0.2516142,0.0004771286,0.0001476796,0.0000225925,0.0004472087,0.7406005,0.0009855904,0.00002795463,0.00003382826,0.0001788856],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949291,0.001140995,0.001539391,0.00005966318,0.00009086385,0.0006112946,0.00008536065,0.0001064506,0.001436903],"genre_scores_gemma":[0.9952829,0.0002747268,0.002474647,0.0002658846,0.0001190189,0.000273803,0.0005840981,0.00003717864,0.0006877188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7385419,"threshold_uncertainty_score":0.8392593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120114643842309,"score_gpt":0.2996947554531287,"score_spread":0.2784936090147057,"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."}}