{"id":"W2902168363","doi":"10.1186/s12913-018-3714-5","title":"Practical utility of general practice data capture and spatial analysis for understanding COPD and asthma","year":2018,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"Australian Primary Health Care Research Institute, Australian National University; Australian National University","keywords":"Medicine; Asthma; COPD; Logistic regression; Descriptive statistics; Bayes' theorem; Environmental health; Statistics; Internal medicine; Bayesian probability","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":[0.005751747,0.0001253876,0.0004924384,0.0002677803,0.0002914841,0.00006272962,0.0002194091,0.0001004102,0.00009152356],"category_scores_gemma":[0.001146386,0.0001095104,0.00003551788,0.0007645885,0.0003903712,0.0003759127,0.0005556449,0.0002974102,0.000004039054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057006,"about_ca_system_score_gemma":0.00112095,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008401088,"about_ca_topic_score_gemma":0.01807692,"domain_scores_codex":[0.9967075,0.0008126542,0.0004184417,0.0006944958,0.0008232594,0.0005436452],"domain_scores_gemma":[0.9955965,0.001784744,0.000201281,0.001100506,0.0007583899,0.0005586334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01420178,0.0009921468,0.9418086,0.01864046,0.001441093,0.00004183621,0.003860006,0.000001939387,0.0001602304,0.002309144,0.008516527,0.008026173],"study_design_scores_gemma":[0.002429962,0.0009372171,0.677791,0.0001956071,0.0002718165,0.00004376304,0.008195453,0.2826112,0.00002618891,0.0002292561,0.02710972,0.0001588193],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6328434,0.006678607,0.3145175,0.02443969,0.0003221687,0.007898562,0.009338282,0.0001767514,0.003784978],"genre_scores_gemma":[0.9690831,0.0002821652,0.02851338,0.0007741239,0.0002810188,0.00001663111,0.0009832029,0.00001787461,0.00004855314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3362396,"threshold_uncertainty_score":0.9998406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2433998558506877,"score_gpt":0.5207706136462523,"score_spread":0.2773707577955646,"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."}}