{"id":"W2777955464","doi":"10.5770/cgj.20.293","title":"Proceedings of the Canadian Frailty Network Summit: Medication Optimization for Frail Older Canadians, Toronto, Monday April 24, 2017","year":2017,"lang":"en","type":"article","venue":"Canadian Geriatrics Journal","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; University of Toronto; Dalhousie University; University of Calgary; Government of Canada; Bruyère; University of Ottawa; McGill University Health Centre; Canadian Institute for Health Information; McMaster University; Queen's University","funders":"Canadian Frailty Network; Government of Canada","keywords":"Summit; Medicine; Polypharmacy; Gerontology; Medical prescription; Geriatrics; Government (linguistics); Health care; Older people; Excellence; Family medicine; Nursing; Psychiatry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008627998,0.0001901097,0.0003044285,0.0001483791,0.002164985,0.0003081606,0.0005932778,0.0002651895,0.0005841817],"category_scores_gemma":[0.00135764,0.0001515022,0.0001777306,0.0001306421,0.0001273396,0.0005647443,0.00002553272,0.0004967713,0.000004005609],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001804216,"about_ca_system_score_gemma":0.005890754,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8137355,"about_ca_topic_score_gemma":0.9793698,"domain_scores_codex":[0.9981506,0.00002334625,0.0004622778,0.000221992,0.0004019995,0.0007397864],"domain_scores_gemma":[0.9958552,0.00007248107,0.0007201959,0.0003485134,0.0006795138,0.002324119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009883704,0.00003432063,0.3866852,0.000171438,0.0002495558,0.00002891252,0.0009747324,0.001021916,0.00001626742,0.00123743,0.5996565,0.009824905],"study_design_scores_gemma":[0.002174824,0.0001169102,0.1939577,0.0001689223,0.0005280997,0.000111979,0.0002672352,0.01388404,0.00003215973,0.0002637011,0.788161,0.0003334879],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3140113,0.01691309,0.002322374,0.377297,0.04397142,0.01179821,0.00200882,0.0001005887,0.2315772],"genre_scores_gemma":[0.9893686,0.0005207494,0.002698462,0.003599823,0.001875576,0.00002210057,0.00002844369,0.00004349525,0.001842776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6753572,"threshold_uncertainty_score":0.999745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07419456032950968,"score_gpt":0.3397956138627404,"score_spread":0.2656010535332307,"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."}}