{"id":"W2953867114","doi":"10.1186/s12911-019-0845-5","title":"Combining population-based administrative health records and electronic medical records for disease surveillance","year":2019,"lang":"en","type":"article","venue":"BMC Medical Informatics and Decision Making","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; George & Fay Yee Centre for Healthcare Innovation","funders":"Canadian Institutes of Health Research; Research Manitoba","keywords":"Medicine; Population; Medical record; Statistics; Standard error; Mean squared error; Mathematics; Internal medicine; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.05353758,0.00127365,0.001866088,0.005284667,0.0004438423,0.002622619,0.001637662,0.0007248914,0.001035138],"category_scores_gemma":[0.1424277,0.0007370727,0.002800457,0.008415909,0.0004333253,0.001789863,0.002247206,0.001024241,0.0004007018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001442801,"about_ca_system_score_gemma":0.002910812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01685254,"about_ca_topic_score_gemma":0.01538955,"domain_scores_codex":[0.9100501,0.06544527,0.005050792,0.004603821,0.01424312,0.0006068923],"domain_scores_gemma":[0.8843021,0.07753202,0.01344089,0.009825882,0.01424205,0.0006571166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004834372,0.000319009,0.6477968,0.00155365,0.01194543,0.0001730076,0.0004929735,0.05405175,0.0007374919,0.002310017,0.003954005,0.2761824],"study_design_scores_gemma":[0.0005256657,0.002372619,0.6779196,0.001795628,0.009493536,0.0007885782,0.0006430958,0.2734678,0.003487147,0.01225062,0.01684703,0.0004085813],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4538576,0.009634564,0.5005062,0.002709916,0.0007741001,0.003808452,0.01537096,0.001559118,0.01177918],"genre_scores_gemma":[0.7678059,0.002299991,0.2210528,0.0006498286,0.000300339,0.001166764,0.006154534,0.00006207109,0.0005078103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05353758,"threshold_uncertainty_score":0.2831373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03067328321029164,"score_gpt":0.3701466327592468,"score_spread":0.3394733495489551,"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."}}