{"id":"W2605606480","doi":"10.23889/ijpds.v1i1.144","title":"The Nascent Pan-Canadian Real-world Health Data Network (PRHDN)","year":2017,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences","funders":"","keywords":"Legislature; Data sharing; Census; Data science; Knowledge translation; Plan (archaeology); Population; Computer science; Business; Knowledge management; Geography; Medicine; Environmental health","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":[0.03556338,0.0008918904,0.0009092036,0.006855439,0.007825239,0.01140629,0.008667533,0.002111715,0.02683078],"category_scores_gemma":[0.06865747,0.001039597,0.0009518824,0.01227536,0.003873585,0.006013346,0.01210393,0.003916787,0.006368185],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09832182,"about_ca_system_score_gemma":0.3058334,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9759851,"about_ca_topic_score_gemma":0.9767597,"domain_scores_codex":[0.9753475,0.005108382,0.00161958,0.003804312,0.01101624,0.003103937],"domain_scores_gemma":[0.8575307,0.01221019,0.003641333,0.01651411,0.08465339,0.02545028],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002462353,0.00004353297,0.01364904,0.0006778039,0.0001132988,0.0001013431,0.001360117,0.001251196,0.0003524411,0.08490997,0.7887272,0.1085679],"study_design_scores_gemma":[0.00005443445,0.00001487892,0.01360307,0.0007814634,0.00002862402,0.00004356783,0.001035798,0.002096134,0.0002971381,0.005716229,0.9762423,0.0000863341],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01693661,0.01086591,0.05688921,0.2099888,0.004754985,0.003388483,0.4597952,0.01015658,0.2272242],"genre_scores_gemma":[0.1793421,0.009070941,0.2757022,0.04482858,0.001213353,0.004563403,0.3759649,0.002536185,0.1067784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9644367,"threshold_uncertainty_score":0.7133781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2889966618631031,"score_gpt":0.572883782227088,"score_spread":0.2838871203639849,"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."}}