{"id":"W2153765714","doi":"10.1111/j.0008-3658.2005.00096.x","title":"Diagnostic uncertainty and medical geography: what are we mapping?","year":2005,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services; Alberta Health; University of Alberta","funders":"","keywords":"Health geography; Medical diagnosis; Confounding; Scale (ratio); Geography; Representation (politics); Space (punctuation); Disease; Data science; Health care; Regional science; Cartography; Medicine; Public health; Health policy; Computer science; International health; Pathology; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05866894,0.00140101,0.003289748,0.01199625,0.004486742,0.01528918,0.005484919,0.005041043,0.002954645],"category_scores_gemma":[0.3549212,0.001307291,0.001693117,0.0186783,0.02481059,0.02718409,0.00843492,0.006321262,0.0003708742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01242743,"about_ca_system_score_gemma":0.01593786,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1614237,"about_ca_topic_score_gemma":0.08042422,"domain_scores_codex":[0.9635548,0.02589493,0.00186326,0.002281125,0.005228731,0.001177184],"domain_scores_gemma":[0.7152047,0.2323122,0.01835782,0.01224175,0.01816098,0.00372268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001423914,0.0000700511,0.1105506,0.002460374,0.00123846,0.0004177126,0.01339424,0.01200761,0.00007550984,0.3213675,0.03944239,0.4988332],"study_design_scores_gemma":[0.00003585367,0.0000289375,0.01457803,0.002937524,0.0002047364,0.000314987,0.006884933,0.006549856,0.00007053698,0.9437651,0.02450903,0.0001205298],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.02672891,0.1339776,0.1138061,0.7087393,0.002203862,0.0001795553,0.001275271,0.0002959832,0.0127935],"genre_scores_gemma":[0.7662289,0.09555335,0.1034272,0.02636394,0.005622796,0.0004537689,0.0008870275,0.0001713722,0.001291699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8385763,"threshold_uncertainty_score":0.3209682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009071120907510338,"score_gpt":0.2170472241390348,"score_spread":0.2079761032315244,"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."}}