{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.01718742,0.0001561026,0.0002033742,0.0008331821,0.00155133,0.001584878,0.01658336,0.00002756258,0.0005287761],"category_scores_gemma":[0.009897495,0.00013329,0.00008936032,0.0006209705,0.000132446,0.00484185,0.006654784,0.0003087629,0.00004514415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000319181,"about_ca_system_score_gemma":0.0002737458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001031125,"about_ca_topic_score_gemma":0.0001383535,"domain_scores_codex":[0.9924442,0.00019836,0.001212747,0.00125414,0.004537096,0.0003534932],"domain_scores_gemma":[0.9941422,0.001164979,0.0009479825,0.002841748,0.0007167674,0.0001862653],"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":[0.0004378703,0.0004095894,0.001179061,0.000006345108,0.00005982078,0.00001518361,0.0002367361,0.007449191,0.0001903668,0.02884911,0.2833042,0.6778625],"study_design_scores_gemma":[0.000586903,0.0002476032,0.004567837,0.00001504481,0.00001353392,0.00002684109,0.000624906,0.09449528,0.00002594703,0.01639918,0.8828146,0.0001823556],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1407422,0.0001140721,0.6445376,0.02465909,0.05348919,0.002891039,0.1319569,0.0001395618,0.001470322],"genre_scores_gemma":[0.9164508,0.00006057353,0.05410666,0.005275019,0.001540953,0.0001215513,0.02059985,0.00003566079,0.001808907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7757086,"threshold_uncertainty_score":0.9997485,"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."}}