{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001235518,0.0001010735,0.0001770198,0.0007948909,0.00006312809,0.00001759181,0.00002851867,0.00006464742,0.00008286789],"category_scores_gemma":[0.00002095942,0.0000941516,0.00005366202,0.001028756,0.00002251188,0.00003804878,0.00003831723,0.0002585729,0.000005518345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001930632,"about_ca_system_score_gemma":0.00001158758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001187302,"about_ca_topic_score_gemma":0.0001151635,"domain_scores_codex":[0.9993618,0.0000272438,0.0001160337,0.0002166904,0.0001053049,0.0001729578],"domain_scores_gemma":[0.9998251,0.00002922843,0.0000324193,0.00004962792,0.00001573499,0.00004785051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002954625,0.00007349952,0.9790316,0.0001304918,0.00002661025,0.00002580927,0.00003666784,0.000002535563,0.00005996907,0.001159908,0.004020875,0.01540245],"study_design_scores_gemma":[0.001152,0.0001497735,0.9459459,0.00008750548,0.0001105335,0.00001064144,0.00005359163,0.0003037931,0.00005610445,0.0002344875,0.05181153,0.00008412141],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9192969,0.01750165,0.000555696,0.003720968,0.0001539839,0.0007947134,0.000003840129,0.0002089869,0.05776327],"genre_scores_gemma":[0.9926549,0.004901457,0.0004003135,0.0005455691,0.00005629538,0.00003218869,0.00002003261,0.000009154498,0.001380114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07335798,"threshold_uncertainty_score":0.3839391,"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."}}