{"id":"W4403122659","doi":"10.1038/s41746-024-01267-6","title":"Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework","year":2024,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"Trinity College Dublin; European Commission","keywords":"Usability; Linkage (software); Disease; Climate change; Data science; Environmental health; Medicine; Computer science; Risk analysis (engineering); Environmental resource management; Ecology; Biology; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001351802,0.0001196755,0.000129961,0.00003042379,0.0000406354,0.00004291367,0.0003099003,0.0000933159,0.00001229692],"category_scores_gemma":[0.0004851866,0.00006042876,0.00002448486,0.00008256939,0.0002941327,0.000006207817,0.0001851933,0.000117855,0.000004150929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000101517,"about_ca_system_score_gemma":0.00003933834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003795102,"about_ca_topic_score_gemma":0.000005118173,"domain_scores_codex":[0.9991162,0.00001404021,0.0001326059,0.0003843393,0.0001595174,0.0001933311],"domain_scores_gemma":[0.9992206,0.00019471,0.00002228493,0.0004864408,0.000009148603,0.00006685553],"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":[0.0004924914,0.0002582469,0.006548372,0.0005355286,0.0003313695,0.0001902137,0.0009640753,0.00009287163,0.003837204,0.000912963,0.1188874,0.8669493],"study_design_scores_gemma":[0.0005044696,0.0009654799,0.001955762,0.0005910291,0.00005202955,0.00001665738,0.001351241,0.0003007172,0.0003772126,0.0009192368,0.9928022,0.0001639478],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6067375,0.1757732,0.1116436,0.09681786,0.001196941,0.002030447,0.001805124,0.0002618005,0.003733615],"genre_scores_gemma":[0.9967499,0.0003896271,0.000539422,0.0004306805,0.0005891361,0.00004624247,0.000632831,0.00001700853,0.0006051362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8739148,"threshold_uncertainty_score":0.2464213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02521840208442248,"score_gpt":0.2957402752718507,"score_spread":0.2705218731874282,"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."}}