{"id":"W2902726914","doi":"10.1186/s12911-018-0699-2","title":"Combination of conditional random field with a rule based method in the extraction of PICO elements","year":2018,"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":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Precision and recall; Conditional random field; Information extraction; Element (criminal law); Data mining; Health informatics; Process (computing); Artificial intelligence; Information retrieval; Health care","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":[0.008128014,0.00161804,0.001851656,0.00834051,0.0007528135,0.001832144,0.001775996,0.00206224,0.002593705],"category_scores_gemma":[0.02490801,0.0004462694,0.002444064,0.003928402,0.0007666408,0.002091873,0.0008226509,0.001594175,0.001600269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008690266,"about_ca_system_score_gemma":0.00216945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006604322,"about_ca_topic_score_gemma":0.006171106,"domain_scores_codex":[0.9935435,0.002311181,0.0009406841,0.001510296,0.001502503,0.000191884],"domain_scores_gemma":[0.96648,0.02829771,0.001034035,0.001025193,0.002941719,0.0002212763],"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":[0.0004921529,0.0003177054,0.006834822,0.001129202,0.0004604652,0.0008285434,0.0002440727,0.03589112,0.008286726,0.002830254,0.008015075,0.9346698],"study_design_scores_gemma":[0.0001611034,0.0003244473,0.007577785,0.0004860703,0.0005867752,0.001369769,0.0001677216,0.942081,0.01943479,0.01428762,0.01335098,0.0001717697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02763894,0.004510194,0.9569788,0.0006958912,0.0004218266,0.0007080366,0.00159421,0.004976636,0.00247561],"genre_scores_gemma":[0.1853397,0.001263371,0.8052093,0.0004829871,0.0003737847,0.0006516372,0.004377433,0.0002670965,0.002034585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00834051,"threshold_uncertainty_score":0.04298556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0246132094954036,"score_gpt":0.370017778471314,"score_spread":0.3454045689759104,"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."}}