{"id":"W4402390590","doi":"10.23889/ijpds.v9i5.2514","title":"Improving Cardiac Insights: Harnessing Privacy-Preserving Record Linkage (PPRL) to Obtain, Link, and Enhance Data for a Healthier Australia","year":2024,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Victoria Park","funders":"","keywords":"Record linkage; Link (geometry); Linkage (software); Computer science; Data mining; Computer network; Medicine; Environmental health; Genetics; Biology; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05788701,0.00070006,0.0007544829,0.004275607,0.001958971,0.007685116,0.003058557,0.001823836,0.002678365],"category_scores_gemma":[0.142945,0.0006513071,0.001384191,0.004872136,0.002331308,0.01185187,0.01707555,0.002888679,0.001608043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001522072,"about_ca_system_score_gemma":0.006816949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003133194,"about_ca_topic_score_gemma":0.003474677,"domain_scores_codex":[0.9357501,0.04369623,0.003328045,0.004315731,0.0114429,0.001466929],"domain_scores_gemma":[0.8833179,0.05169427,0.0100595,0.03776934,0.01481421,0.002344786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004719081,0.0004127843,0.02550639,0.001119633,0.0002873224,0.0005714232,0.01137116,0.006336074,0.006273139,0.03633117,0.01554561,0.8957734],"study_design_scores_gemma":[0.0004448974,0.002871681,0.03197921,0.003591073,0.0008078677,0.004707681,0.0201684,0.1246652,0.05649944,0.3513712,0.4021578,0.0007355481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09862402,0.004632168,0.838464,0.03164246,0.0005411026,0.002109117,0.002152426,0.005774897,0.01606],"genre_scores_gemma":[0.2907372,0.002452618,0.6964091,0.003521562,0.0003024275,0.0007453184,0.001952558,0.0003496423,0.003529466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05788701,"threshold_uncertainty_score":0.3061394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3552699534054246,"score_gpt":0.5394998433459082,"score_spread":0.1842298899404836,"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."}}