{"id":"W2981490137","doi":"10.9778/cmajo.20190065","title":"Identifying Ontario geographic regions to assess adults who present to hospital with laboratory-defined conditions: a descriptive study","year":2019,"lang":"en","type":"article","venue":"CMAJ Open","topic":"Clinical Laboratory Practices and Quality Control","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Catchment area; Emergency department; Descriptive statistics; Geographic information system; Medical emergency; Population; Medicine; Quarter (Canadian coin); Health care; Geography; Environmental health; Drainage basin; Cartography; Nursing; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0008772074,0.0002570899,0.0007029133,0.0001675291,0.0001844998,0.0004468331,0.0004548512,0.00008856629,0.0006234038],"category_scores_gemma":[0.000585174,0.0002072356,0.00008173613,0.000808991,0.00004166439,0.000737711,0.00039467,0.0004438463,0.0005473807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002252633,"about_ca_system_score_gemma":0.0007406792,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04893177,"about_ca_topic_score_gemma":0.1390273,"domain_scores_codex":[0.9975526,0.0002650004,0.0005205065,0.0007651927,0.0005188548,0.0003778981],"domain_scores_gemma":[0.9969877,0.0003754303,0.0002240508,0.0009872044,0.0008552775,0.0005703369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003094868,0.00237053,0.9707711,0.00005484903,0.0006192807,0.000179922,0.01538801,0.00001478048,0.0001870762,0.001456479,0.005696859,0.0001662166],"study_design_scores_gemma":[0.007961986,0.007101582,0.9148201,0.0007533086,0.0003890195,0.000004404641,0.02126558,0.00001038231,0.00003345418,0.00005946202,0.04720188,0.0003988574],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820688,0.00003724391,0.0001381761,0.006689426,0.0003032493,0.008967748,0.00009483351,0.00005755974,0.001642986],"genre_scores_gemma":[0.9911362,0.000003568862,0.001025177,0.003386953,0.0001117846,0.0007655224,0.00003863302,0.00004048063,0.003491701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09009556,"threshold_uncertainty_score":0.9574015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0908772245555557,"score_gpt":0.3905403397929378,"score_spread":0.2996631152373821,"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."}}