{"id":"W2124468198","doi":"10.1109/titb.2010.2103954","title":"Predicting Breast Screening Attendance Using Machine Learning Techniques","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Artificial neural network; Attendance; Algorithm; Data mining","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001446502,0.0006202355,0.0005890143,0.002842922,0.0003010228,0.0007069973,0.0007089363,0.0008316209,0.001119139],"category_scores_gemma":[0.005796373,0.000267717,0.000576565,0.00167317,0.0001720261,0.0007026262,0.000436443,0.0007201092,0.0005657416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000556678,"about_ca_system_score_gemma":0.0006405544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008015147,"about_ca_topic_score_gemma":0.009443665,"domain_scores_codex":[0.9993731,0.0002091928,0.00005368719,0.0001083411,0.0001927088,0.0000628771],"domain_scores_gemma":[0.9975823,0.00163727,0.0002621535,0.0001169809,0.0003383812,0.00006293369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002749367,0.0005999738,0.1213449,0.0001178919,0.0002264602,0.0001772119,0.00008236829,0.4087558,0.004084622,0.001023247,0.003480148,0.4598324],"study_design_scores_gemma":[0.00001133037,0.00005217422,0.01017929,0.0000130669,0.00002388906,0.00005317648,0.00001840822,0.9867184,0.001419117,0.0009938106,0.000504313,0.0000130445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5168788,0.00135941,0.4720629,0.001093053,0.0001006296,0.0001583075,0.001118764,0.003381956,0.003846181],"genre_scores_gemma":[0.8336341,0.0004230475,0.1627695,0.00008418822,0.0001022042,0.00007220923,0.001171457,0.00003670091,0.001706647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008015147,"threshold_uncertainty_score":0.01593703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04806660619266561,"score_gpt":0.2997155179202843,"score_spread":0.2516489117276187,"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."}}