{"id":"W6958478165","doi":"10.6084/m9.figshare.19533984","title":"Additional file 1 of Developing a random forest algorithm to identify patent foramen ovale and atrial septal defects in Ontario administrative databases","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Cardiovascular and Diving-Related Complications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Canadian Institute for Advanced Research; University Health Network; University of Toronto","funders":"","keywords":"Patent foramen ovale; Random forest; Baseline (sea); Table (database)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00141194,0.0004630011,0.000601103,0.001362535,0.0005937738,0.0008035318,0.001306152,0.0005478582,0.7177626],"category_scores_gemma":[0.02492763,0.0004701329,0.000581124,0.002271131,0.0001804038,0.0007883376,0.0005512898,0.0005006464,0.07852637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00175818,"about_ca_system_score_gemma":0.00291172,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06708614,"about_ca_topic_score_gemma":0.1199032,"domain_scores_codex":[0.9995623,0.00006946847,0.00006940338,0.0001256612,0.0001060406,0.00006718606],"domain_scores_gemma":[0.9910486,0.00647695,0.000484577,0.0004598369,0.001345816,0.0001842459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001941643,0.00004898136,0.0083533,0.0006213477,0.00003748825,0.00005920993,0.00005797507,0.00180738,0.00006458193,0.00099976,0.9700984,0.01765742],"study_design_scores_gemma":[0.005495365,0.0002685197,0.07376331,0.002339312,0.0002828697,0.0005307703,0.0004663502,0.04356441,0.001366757,0.01546914,0.8563179,0.000135371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0007731385,0.00001269235,0.001794407,0.0001207325,0.00001656642,0.0002304271,0.993975,0.0006032626,0.002473819],"genre_scores_gemma":[0.04812849,0.0001099315,0.022528,0.0003688709,0.0001088365,0.002841011,0.9099481,0.001326746,0.01464003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9329138,"threshold_uncertainty_score":0.4025774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124581451204138,"score_gpt":0.312379442336959,"score_spread":0.1999212972165452,"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."}}