{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003959846,0.0002332405,0.0003603995,0.002076199,0.0002216529,0.00001149558,0.0001709347,0.0003340078,0.0001568092],"category_scores_gemma":[0.00003090205,0.0002142835,0.00007199242,0.001722987,0.0002791301,0.0007350127,0.000005117489,0.0009655908,0.00002505888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000212285,"about_ca_system_score_gemma":0.00006619919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00102981,"about_ca_topic_score_gemma":0.00005179954,"domain_scores_codex":[0.9983053,0.0000246142,0.0007105406,0.0002147666,0.0003494258,0.0003953687],"domain_scores_gemma":[0.9991712,0.00002601788,0.0002404004,0.000283729,0.0001772053,0.0001014414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002191116,0.0004483867,0.2901163,0.0003975659,0.000283377,0.0001872643,0.003718416,0.001154649,0.03345446,0.0003525963,0.00005989055,0.667636],"study_design_scores_gemma":[0.01194646,0.006366869,0.04017562,0.008765181,0.0006493329,0.01314013,0.01538497,0.169498,0.7276013,0.0008326471,0.003857501,0.001781886],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2414786,0.0001095046,0.7540451,0.00129799,0.000241414,0.0005596376,0.0000336226,0.001028718,0.00120533],"genre_scores_gemma":[0.9646751,0.00008083081,0.03456713,0.0005128838,0.00003821349,0.00005521139,0.00001722661,0.0000175083,0.00003587615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7231965,"threshold_uncertainty_score":0.8738226,"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."}}