{"id":"W2084946528","doi":"10.1007/s10791-006-9020-6","title":"Knowledge-based query expansion to support scenario-specific retrieval of medical free text","year":2007,"lang":"en","type":"article","venue":"Information Retrieval","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":79,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institutes of Health; McMaster University","keywords":"Computer science; Query expansion; Unified Medical Language System; Information retrieval; Query language; Testbed; Query optimization; Precision and recall; Exploit; Recall; Data mining; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001597605,0.0006491434,0.0007664282,0.002762076,0.0004870538,0.001115879,0.001326372,0.001146939,0.00836336],"category_scores_gemma":[0.008567872,0.0003156996,0.0007312807,0.001769121,0.00032029,0.00238987,0.001527379,0.0006612243,0.00248776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006379624,"about_ca_system_score_gemma":0.0008615773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003199656,"about_ca_topic_score_gemma":0.004793975,"domain_scores_codex":[0.9988093,0.00043616,0.0001576911,0.0001853595,0.0003192354,0.00009224516],"domain_scores_gemma":[0.9966466,0.002170275,0.0001400595,0.0003477118,0.0005828404,0.0001124321],"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":[0.001815468,0.0009832729,0.006045635,0.001499178,0.0002964464,0.002838877,0.001911335,0.06046873,0.08260421,0.02281441,0.09860475,0.7201176],"study_design_scores_gemma":[0.0003096825,0.0002122091,0.003352615,0.0001591405,0.0002594514,0.001807261,0.0007680311,0.8807873,0.04234467,0.02346915,0.04642054,0.000109924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09532925,0.001011649,0.856819,0.00187224,0.0001984119,0.0010242,0.005377531,0.02643069,0.01193707],"genre_scores_gemma":[0.468316,0.0006388643,0.5130407,0.0006193874,0.0001455141,0.0005836184,0.01272979,0.0006388624,0.00328711],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00836336,"threshold_uncertainty_score":0.02797824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02053831162805873,"score_gpt":0.3008048304354492,"score_spread":0.2802665188073905,"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."}}