{"id":"W2809441073","doi":"10.23889/ijpds.v3i2.550","title":"Building a Pan-Canadian Real World Health Data Network","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Manitoba; University of New Brunswick; Canadian Institute for Health Information; University of British Columbia","funders":"","keywords":"Benchmarking; Harmonization; Data access; Indigenous; Business; Computer science; Data science; Public relations; Political science; Database; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007399408,0.0001283359,0.0002151978,0.000618743,0.004013823,0.0001939671,0.005031791,0.00005738743,0.0003753104],"category_scores_gemma":[0.001187481,0.0001152406,0.00002980312,0.0006618588,0.0001623896,0.003316941,0.001321724,0.0004649164,0.0001018811],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002021796,"about_ca_system_score_gemma":0.00933229,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1135991,"about_ca_topic_score_gemma":0.5177544,"domain_scores_codex":[0.9964556,0.0001566532,0.0009406387,0.0005783448,0.0009145427,0.0009541932],"domain_scores_gemma":[0.9962673,0.0003584845,0.000676262,0.00118492,0.000912638,0.0006004307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006945535,0.00001541748,0.1878907,0.00001805491,0.00002759756,0.000004718876,0.0001993973,0.00001800082,0.0000129737,0.04610674,0.684167,0.0814699],"study_design_scores_gemma":[0.0003740504,0.00004099688,0.242346,0.0001181528,0.000006725998,0.00001350986,0.00005718396,0.006734447,4.450078e-7,0.005661925,0.7445278,0.0001187178],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.06952385,0.001071812,0.1479151,0.497891,0.2072429,0.00613896,0.01867681,0.0005709667,0.05096861],"genre_scores_gemma":[0.7103561,0.0006228708,0.1678394,0.08316878,0.02902313,0.00003107002,0.005900774,0.00006339508,0.002994532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6408322,"threshold_uncertainty_score":0.9972828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2647105010337236,"score_gpt":0.56796727186318,"score_spread":0.3032567708294563,"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."}}