{"id":"W3211905090","doi":"10.1016/j.jcjd.2021.09.065","title":"Data on Patient Record Trajectory for Linkage (DataPRinT Linkage)","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Diabetes","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Public Health; North York General Hospital","funders":"","keywords":"Linkage (software); Medicine; Record linkage; Health records; Variety (cybernetics); Medical record; Trajectory; Medical emergency; Genetics; Environmental health; Health care; Internal medicine; Gene; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005443696,0.0008650706,0.00105968,0.009102796,0.001030084,0.00282845,0.001029723,0.00113084,0.04077876],"category_scores_gemma":[0.02913113,0.0004846804,0.001456994,0.01162437,0.0002595952,0.001976182,0.002826923,0.001148622,0.01836242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001657932,"about_ca_system_score_gemma":0.008630638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01878954,"about_ca_topic_score_gemma":0.01625109,"domain_scores_codex":[0.995226,0.0005880356,0.001046472,0.001352441,0.00139559,0.0003914146],"domain_scores_gemma":[0.9868305,0.003865066,0.00179432,0.004149518,0.002822559,0.0005380042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00192672,0.0004592112,0.08621192,0.004537582,0.0008478641,0.001042532,0.001973762,0.006648105,0.01121805,0.01989324,0.4764039,0.3888372],"study_design_scores_gemma":[0.0002785933,0.0002975066,0.08703418,0.000906399,0.0005291251,0.001207743,0.001182221,0.01010326,0.02850815,0.01602848,0.8537384,0.0001859708],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01300158,0.0003615486,0.03966572,0.0006722671,0.0002751398,0.0006624681,0.9310023,0.007868811,0.00649025],"genre_scores_gemma":[0.06493104,0.0006068863,0.08817925,0.0003085913,0.0000973011,0.001630759,0.837193,0.00085461,0.006198538],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04077876,"threshold_uncertainty_score":0.1364185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03695076323179959,"score_gpt":0.2672872227547012,"score_spread":0.2303364595229016,"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."}}