{"id":"W2067284187","doi":"10.1186/1472-6947-10-29","title":"Combining classifiers for robust PICO element detection","year":2010,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Data mining; Identification (biology); Task (project management); Health informatics; Information retrieval; Element (criminal law); Process (computing); Population; Artificial intelligence; Machine learning; Pattern recognition (psychology); Medicine","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":[0.001024058,0.0001148666,0.0001506518,0.00005745314,0.0001695587,0.00005948184,0.0001634862,0.000328535,0.00003344143],"category_scores_gemma":[0.001659854,0.00008831479,0.00006397109,0.0000585675,0.0001437497,0.000005374944,0.0001532322,0.0001960588,0.000003288791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005047802,"about_ca_system_score_gemma":0.00008724205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.368696e-7,"about_ca_topic_score_gemma":0.00007920872,"domain_scores_codex":[0.9988834,0.00001605719,0.0004184445,0.0001384516,0.0003184398,0.0002251933],"domain_scores_gemma":[0.9990445,0.0004167891,0.0001210307,0.0001879021,0.00006061407,0.0001691231],"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":[0.0001277656,0.00002008294,0.0004329835,0.00005087006,0.00001476844,5.328915e-7,0.00006730851,0.00003211075,0.001462243,0.0003558838,0.001162391,0.996273],"study_design_scores_gemma":[0.004313088,0.001058814,0.001481908,0.000327336,0.00005440591,0.0001412997,0.00293375,0.8409411,0.004356825,0.005810096,0.1379552,0.0006262022],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3248566,0.00005959092,0.6743225,0.00001235352,0.0004053949,0.00008969365,0.000002898924,0.00001563539,0.0002353054],"genre_scores_gemma":[0.54506,0.00004744167,0.4541243,0.0005166451,0.0001805838,0.00002294217,0.00001532166,0.000008727965,0.00002403413],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9956468,"threshold_uncertainty_score":0.3601373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03433126563978311,"score_gpt":0.3221974987188929,"score_spread":0.2878662330791098,"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."}}