{"id":"W3200024623","doi":"10.1370/afm.2716","title":"Technology-Enabled and Artificial Intelligence Support for Pre-Visit Planning in Ambulatory Care: Findings From an Environmental Scan","year":2021,"lang":"en","type":"article","venue":"The Annals of Family Medicine","topic":"Simulation-Based Education in Healthcare","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Health care; Implementation; Nursing; Knowledge management; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004665458,0.0001388386,0.0003560029,0.0001919398,0.00009498762,0.000005140273,0.0001160586,0.0001301265,0.000108484],"category_scores_gemma":[0.0002200308,0.0001106661,0.00003775824,0.0002793352,0.0003154256,0.00006548372,0.00003562822,0.0002296343,0.000002204721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006235007,"about_ca_system_score_gemma":0.0002339583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000244957,"about_ca_topic_score_gemma":0.00001472615,"domain_scores_codex":[0.9986113,0.00004831504,0.0005194306,0.0003152729,0.0002649592,0.0002407055],"domain_scores_gemma":[0.9987658,0.0004255864,0.0001199777,0.0004140752,0.000153602,0.0001210152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001051183,0.0002594504,0.857897,0.0004070768,0.0001090229,0.00003532277,0.0494816,0.0006324771,0.05655263,0.0004945241,0.001512466,0.03156721],"study_design_scores_gemma":[0.0008714378,0.000981249,0.8377607,0.0008429064,0.0001072068,0.00001547908,0.09917398,0.002248677,0.05282279,0.004340914,0.0006597072,0.0001748844],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805006,0.005257026,0.0002502323,0.01317891,0.0001864499,0.0005087306,0.00004067738,0.00002861676,0.00004876964],"genre_scores_gemma":[0.996012,0.0001628733,0.0004874085,0.002687206,0.0002472122,0.00004226621,0.0002881126,0.00002016415,0.00005273803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04969237,"threshold_uncertainty_score":0.4512831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1946532550113832,"score_gpt":0.454742870226195,"score_spread":0.2600896152148118,"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."}}