{"id":"W4294243567","doi":"10.23889/ijpds.v7i3.1782","title":"Data on Patient Record Trajectory for Linkage (DataPRinT Linkage).","year":2022,"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":"University of Toronto","funders":"","keywords":"Linkage (software); Computer science; Data mining; Table (database); Key (lock); Medical diagnosis; Process (computing); Medical record; Health care; Record linkage; Information retrieval; Data science; Computer security; Medicine; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.04458271,0.001061447,0.001584015,0.01210909,0.002451248,0.004045127,0.002510293,0.001839829,0.04743524],"category_scores_gemma":[0.147128,0.0009333423,0.002611674,0.01961118,0.0006198853,0.004735452,0.009239126,0.002587411,0.01800519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003977864,"about_ca_system_score_gemma":0.02317622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01849136,"about_ca_topic_score_gemma":0.0157285,"domain_scores_codex":[0.9591243,0.01349642,0.007597347,0.005837252,0.012232,0.001712707],"domain_scores_gemma":[0.8785658,0.02908006,0.02178591,0.04287229,0.02483477,0.002861226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001294762,0.0003516773,0.115312,0.004647356,0.001569952,0.0002971751,0.002747497,0.003146249,0.002457723,0.04546516,0.3837754,0.438935],"study_design_scores_gemma":[0.0004455772,0.0003294446,0.1239532,0.002099286,0.0005832674,0.0006979032,0.00150108,0.005693,0.00636062,0.02762282,0.8304513,0.0002624678],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01935581,0.0019984,0.2360256,0.005467546,0.001448641,0.00796577,0.6859449,0.007428993,0.03436434],"genre_scores_gemma":[0.08380283,0.00165703,0.2850526,0.001818098,0.0004304726,0.01673053,0.5946251,0.001429253,0.01445418],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.04743524,"threshold_uncertainty_score":0.2357787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4308056502261164,"score_gpt":0.5122798057911399,"score_spread":0.08147415556502347,"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."}}