{"id":"W4386757164","doi":"10.2196/45376","title":"Developing a Semantically Based Query Recommendation for an Electronic Medical Record Search Engine: Query Log Analysis and Design Implications","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; University of Cincinnati","keywords":"Computer science; Query expansion; Information retrieval; Web search query; Query language; Scalability; Web query classification; Search engine; Sargable; Query optimization; Process (computing); Feature (linguistics); RDF query language; World Wide Web; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00918851,0.0001616955,0.0002625095,0.001979223,0.0008053188,0.000435345,0.0008645281,0.0001620044,0.00006008966],"category_scores_gemma":[0.0004799761,0.0001415528,0.0001029275,0.005889995,0.0001468957,0.00169999,0.0003593308,0.000648329,0.00009421368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003476261,"about_ca_system_score_gemma":0.001564652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005321022,"about_ca_topic_score_gemma":0.00007127144,"domain_scores_codex":[0.9960982,0.0007934705,0.0005204417,0.0004106387,0.001076461,0.001100841],"domain_scores_gemma":[0.996033,0.001985661,0.00007215473,0.000431705,0.001110799,0.0003666886],"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":[0.0002327095,0.000329607,0.00223928,0.0003544309,0.000314103,0.000006395752,0.006985421,0.0003144012,0.0008159333,0.1481726,0.004991257,0.8352439],"study_design_scores_gemma":[0.0005749709,0.0006980406,0.03150414,0.00003054122,0.00001377221,0.000005771564,0.0003525082,0.9581563,0.001665148,0.004473794,0.002279585,0.000245403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04318895,0.000004688848,0.9428498,0.01245908,0.00003723722,0.00110877,0.00002053257,0.0002200835,0.0001108529],"genre_scores_gemma":[0.9533709,0.0001603807,0.04404355,0.0003701162,0.00005769177,0.001404265,0.0004624536,0.00002119985,0.0001094452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9578419,"threshold_uncertainty_score":0.6193942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1403644557443018,"score_gpt":0.451414670744817,"score_spread":0.3110502150005152,"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."}}