{"id":"W4251515687","doi":"10.2196/preprints.16777","title":"Translating Clinical Questions by Physicians Into Searchable Queries: Analytical Survey Study (Preprint)","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Health Sciences Research and Education","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Impact","funders":"","keywords":"Information retrieval; Context (archaeology); Computer science; MEDLINE; Preprint; World Wide Web; 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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03041928,0.0004465673,0.0009265481,0.006909784,0.001171435,0.002840878,0.0008693127,0.001202418,0.006477566],"category_scores_gemma":[0.2085173,0.0007764499,0.001127506,0.009942665,0.001661811,0.003554277,0.001997492,0.001142409,0.004124305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002608981,"about_ca_system_score_gemma":0.005294329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006101155,"about_ca_topic_score_gemma":0.005455977,"domain_scores_codex":[0.9578215,0.0199272,0.01290799,0.003185945,0.004577158,0.001580103],"domain_scores_gemma":[0.664344,0.2561326,0.0494655,0.007226082,0.02005278,0.002779004],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001987263,0.002394271,0.8714237,0.004763107,0.0004390466,0.0004703813,0.04432247,0.0003787309,0.0007148476,0.0007033077,0.01544275,0.05696009],"study_design_scores_gemma":[0.0009775296,0.005199957,0.8979771,0.002580004,0.0004567689,0.001560069,0.06523623,0.002906411,0.001429679,0.0006238408,0.02085039,0.0002019623],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838806,0.0009140645,0.001709803,0.0008855768,0.00003349879,0.003205168,0.007503267,0.0001010517,0.001766921],"genre_scores_gemma":[0.9764957,0.00117463,0.005936546,0.002370349,0.0000918638,0.008541716,0.004369972,0.00008619441,0.000933072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9695807,"threshold_uncertainty_score":0.1608744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.347255119572382,"score_gpt":0.6113090520086675,"score_spread":0.2640539324362855,"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."}}