{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08162078,0.001235581,0.001124948,0.01895206,0.01377167,0.01601536,0.009398213,0.002865413,0.01480708],"category_scores_gemma":[0.1061823,0.001481773,0.001517206,0.02059176,0.004447149,0.01351063,0.02210669,0.003836012,0.004522962],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0968345,"about_ca_system_score_gemma":0.2941519,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9541191,"about_ca_topic_score_gemma":0.9424042,"domain_scores_codex":[0.9371307,0.02240385,0.004603119,0.007192637,0.02163568,0.007033994],"domain_scores_gemma":[0.7764447,0.02153292,0.005004276,0.02196448,0.146386,0.02866764],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003550243,0.0002466328,0.04209773,0.001565935,0.0002411292,0.0006355285,0.007500109,0.009563031,0.001173561,0.1922794,0.5284682,0.2158739],"study_design_scores_gemma":[0.00006830692,0.00005143445,0.01773251,0.001439171,0.0000814818,0.0001157779,0.005930882,0.0110212,0.0007321197,0.01608837,0.9465119,0.0002269358],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.0345541,0.009708788,0.2423007,0.2396344,0.004555746,0.01266696,0.1594971,0.01235816,0.2847239],"genre_scores_gemma":[0.1508742,0.006650612,0.6460846,0.01836919,0.0009054812,0.00475789,0.1378406,0.001731006,0.03278635],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9183792,"threshold_uncertainty_score":0.7025868,"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."}}