{"id":"W4206899722","doi":"10.1007/s41060-021-00296-8","title":"The validation of chest tube management after lung resection surgery using a random forest classifier","year":2022,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ottawa Hospital; University of Ottawa; Dalhousie University","funders":"Department of Medicine, Ottawa Hospital; Ontario Medical Association; Ontario Ministry of Health and Long-Term Care","keywords":"Random forest; Classifier (UML); Lung; Medicine; Chest tube; Resection; Radiology; Computer science; Surgery; Artificial intelligence; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005120871,0.0007459241,0.0008474034,0.001073735,0.0004346173,0.0009705473,0.0008950356,0.001474828,0.0006725489],"category_scores_gemma":[0.01030994,0.0001612128,0.0007651668,0.0004088247,0.0003381156,0.0007179743,0.0003861906,0.0007955623,0.000565499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004206356,"about_ca_system_score_gemma":0.0009019331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003437129,"about_ca_topic_score_gemma":0.003040116,"domain_scores_codex":[0.9982142,0.000510586,0.0002260972,0.0003977436,0.0004430666,0.0002083653],"domain_scores_gemma":[0.9913902,0.004909837,0.0007085415,0.0005901532,0.002094251,0.0003070962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004775579,0.001513718,0.6296408,0.0002081905,0.0005099781,0.0005862159,0.0002283355,0.08304487,0.01570583,0.0003969892,0.005614226,0.2577752],"study_design_scores_gemma":[0.00009704778,0.001713963,0.1874598,0.00007123587,0.0003007356,0.0006434671,0.0002424584,0.7931227,0.01453999,0.0005406986,0.001211809,0.00005608537],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752327,0.0006259247,0.02139924,0.0002172522,0.0002120974,0.00008341733,0.001173944,0.0004074066,0.0006480635],"genre_scores_gemma":[0.9914021,0.00009652568,0.005918839,0.00004849979,0.0000528666,0.00003054424,0.002067429,0.0000228823,0.0003603644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005120871,"threshold_uncertainty_score":0.02708209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06469924393754112,"score_gpt":0.3353173416258536,"score_spread":0.2706180976883125,"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."}}