{"id":"W2955236073","doi":"10.18653/v1/w19-5023","title":"Enhancing PIO Element Detection in Medical Text Using Contextualized Embedding","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Medias Data Services (Canada)","funders":"","keywords":"Embedding; Computer science; Classifier (UML); Artificial intelligence; Encoder; Transformer; Ambiguity; Boosting (machine learning); Machine learning; Leverage (statistics); Population; Pattern recognition (psychology); Data mining; Engineering; Medicine","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":[0.002248833,0.0007084247,0.0006030293,0.002134972,0.0002328919,0.0009403801,0.0005678753,0.001036279,0.001496685],"category_scores_gemma":[0.01500264,0.0002001256,0.0005680423,0.001215366,0.0004246372,0.002130114,0.0009633738,0.001025825,0.001122774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002963603,"about_ca_system_score_gemma":0.0004609327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006487456,"about_ca_topic_score_gemma":0.001313866,"domain_scores_codex":[0.9985405,0.0006405952,0.0001394966,0.0003988988,0.0002041612,0.00007633933],"domain_scores_gemma":[0.9906825,0.006547611,0.001010431,0.00091797,0.0007010496,0.0001404021],"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":[0.0008563165,0.0005318702,0.04052543,0.00133444,0.0002368974,0.0004244825,0.0008428424,0.04752106,0.03420354,0.008090582,0.008513836,0.8569188],"study_design_scores_gemma":[0.00008637319,0.0008987077,0.03108392,0.0004073086,0.0003278274,0.001428991,0.000468266,0.8581505,0.04048211,0.04047924,0.02606717,0.0001195155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1619114,0.005787794,0.8190256,0.001790305,0.0004994675,0.0003088713,0.003904877,0.002336081,0.004435621],"genre_scores_gemma":[0.6909603,0.001458886,0.2980292,0.0004401594,0.0007389089,0.0002252516,0.005257885,0.0002126429,0.002676716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002248833,"threshold_uncertainty_score":0.01189315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03735605152197291,"score_gpt":0.3274413069546671,"score_spread":0.2900852554326941,"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."}}