{"id":"W1888011339","doi":"","title":"Clinical Information Retrieval using Document and PICO Structure","year":2010,"lang":"en","type":"article","venue":"North American Chapter of the Association for Computational Linguistics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Information retrieval; Weighting; Computer science; Document Structure Description; Data mining; Medicine; XML; World Wide Web","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":[0.003358747,0.0009640335,0.001797333,0.01639556,0.001132382,0.002932613,0.001073433,0.001335185,0.002612438],"category_scores_gemma":[0.02494479,0.0006201572,0.001441727,0.01232926,0.0008873081,0.007831783,0.002873014,0.001043484,0.0016898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001784869,"about_ca_system_score_gemma":0.002258616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004895874,"about_ca_topic_score_gemma":0.004865097,"domain_scores_codex":[0.9957455,0.001359225,0.0006999595,0.0007853025,0.0012529,0.0001571201],"domain_scores_gemma":[0.9880932,0.007001623,0.001326407,0.001423159,0.001821496,0.0003340661],"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":[0.001067427,0.0003384002,0.01199412,0.001561504,0.0003679744,0.0004783221,0.001372703,0.028672,0.01812079,0.03614822,0.02262082,0.8772578],"study_design_scores_gemma":[0.0003627515,0.0009802581,0.01262307,0.0004379514,0.0005461806,0.00198758,0.00101213,0.7502884,0.01876292,0.1506119,0.06213248,0.0002543067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09959158,0.008047386,0.8651266,0.002597828,0.0004259,0.001113474,0.008850271,0.006350489,0.007896395],"genre_scores_gemma":[0.4315706,0.002981223,0.5437069,0.0006110973,0.0006779201,0.001177987,0.01450524,0.0004238821,0.004345237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01639556,"threshold_uncertainty_score":0.01776296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01164874062539717,"score_gpt":0.3002182539384063,"score_spread":0.2885695133130092,"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."}}