{"id":"W2760627203","doi":"10.1111/hex.12614","title":"Measuring recall of medical information in non‐English‐speaking people with cancer: A methodology","year":2017,"lang":"en","type":"article","venue":"Health Expectations","topic":"Interpreting and Communication in Healthcare","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Research Institute in Oncology and Hematology; CancerCare Manitoba","funders":"Medical Research Council; National Health and Medical Research Council; Peter MacCallum Cancer Centre","keywords":"Recall; Coding (social sciences); Interpreter; Computer science; Context (archaeology); Medical information; Medical record; Natural language processing; Medicine; Psychology; Medical education; Information retrieval; Radiology; Cognitive psychology; Programming language","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":[0.04555346,0.0009143322,0.0007216377,0.004799709,0.002317282,0.002510371,0.001451065,0.0009947517,0.001434614],"category_scores_gemma":[0.08386666,0.0007515172,0.001170341,0.00456011,0.002165276,0.001643936,0.003273938,0.001018739,0.0004217564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003089022,"about_ca_system_score_gemma":0.008398911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004285959,"about_ca_topic_score_gemma":0.00666696,"domain_scores_codex":[0.9330692,0.04841919,0.009472866,0.002826678,0.005603173,0.0006089732],"domain_scores_gemma":[0.9085045,0.04980164,0.01759038,0.00536593,0.01779817,0.0009394882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000930596,0.0008998514,0.2437401,0.006700026,0.0003094174,0.0008440661,0.3380207,0.0008386473,0.009327866,0.004220285,0.003150967,0.3910175],"study_design_scores_gemma":[0.00089879,0.009620064,0.4311497,0.01058508,0.0008465229,0.00690654,0.377294,0.009389888,0.031725,0.01297542,0.1077177,0.0008913474],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6549823,0.003341102,0.2796308,0.002208964,0.0002107309,0.04504996,0.002539069,0.0002592279,0.01177791],"genre_scores_gemma":[0.4811626,0.002421912,0.4357157,0.0008320481,0.00008877625,0.07651555,0.001209627,0.00005943978,0.00199432],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04555346,"threshold_uncertainty_score":0.2409126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2232036717125574,"score_gpt":0.5130717250328886,"score_spread":0.2898680533203312,"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."}}