{"id":"W4413129210","doi":"10.3390/tomography11080090","title":"Machine Learning and Feature Selection in Pediatric Appendicitis","year":2025,"lang":"en","type":"article","venue":"Tomography","topic":"Appendicitis Diagnosis and Management","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nova Scotia Health Authority; St. Francis Xavier University","funders":"Nova Scotia Health Authority; Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Compute Canada","keywords":"Random forest; Feature selection; Artificial intelligence; Machine learning; Gradient boosting; Computer science; Benchmarking; Logistic regression; Receiver operating characteristic; Feature (linguistics); Stochastic gradient descent; Decision tree; Predictive modelling; Artificial neural network","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":[0.006335384,0.0007434478,0.0006100014,0.001385203,0.0002527789,0.0006303143,0.0005848082,0.0005033445,0.0007666507],"category_scores_gemma":[0.01372598,0.000151869,0.0007922669,0.000990381,0.0003151895,0.0004288514,0.0005627131,0.0008898791,0.000221578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005584307,"about_ca_system_score_gemma":0.000904832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002449592,"about_ca_topic_score_gemma":0.002373,"domain_scores_codex":[0.998246,0.0009981439,0.0001499308,0.0002442419,0.000260962,0.0001007979],"domain_scores_gemma":[0.9905273,0.006962924,0.001068357,0.0004146338,0.0008555037,0.0001712571],"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":[0.0008274027,0.0004160563,0.5754845,0.0003133607,0.0006284951,0.0003168703,0.00009844545,0.1763535,0.001934152,0.0004830134,0.003269604,0.2398746],"study_design_scores_gemma":[0.0001171096,0.00128377,0.1548416,0.0001811389,0.0002786654,0.0008381793,0.0001104039,0.8301278,0.006295119,0.00329666,0.002583193,0.00004641161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9285814,0.004310615,0.06246993,0.001154104,0.0001046069,0.0001616338,0.001886255,0.0004440924,0.0008874859],"genre_scores_gemma":[0.9800829,0.000458306,0.01777148,0.00007591367,0.0000674925,0.00009488025,0.001258426,0.00001644876,0.0001742291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006335384,"threshold_uncertainty_score":0.03350508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004235343611350441,"score_gpt":0.2480712266669653,"score_spread":0.2438358830556148,"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."}}