{"id":"W4385458457","doi":"10.1080/21678707.2023.2241347","title":"Real world data for rare diseases research: The beginner’s guide to registries","year":2023,"lang":"en","type":"article","venue":"Expert Opinion on Orphan Drugs","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Medicine; Disease; Medical record; Rare disease; Summary of Product Characteristics; Disease registry; MEDLINE; Product (mathematics); Family medicine; Medical emergency; Data science; Drug; Pathology; Computer science; Pharmacology","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":[0.0004936312,0.0001713881,0.0001438116,0.0001428421,0.0004268699,0.0001047493,0.001056753,0.00004906362,0.00001989158],"category_scores_gemma":[0.0005661555,0.0001249674,0.00009483442,0.0004090833,0.000173431,0.000006408259,0.0007880355,0.0000713098,0.00008581216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002676522,"about_ca_system_score_gemma":0.0001919814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000163377,"about_ca_topic_score_gemma":0.000095176,"domain_scores_codex":[0.9982635,0.000116589,0.0002075981,0.0006664387,0.0002968966,0.0004489803],"domain_scores_gemma":[0.9977518,0.0001612002,0.00005403726,0.001672763,0.0001501283,0.0002100432],"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":[0.0003194959,0.00005102244,0.0000923375,0.000009323975,0.00003453814,0.00000349836,0.0001524767,0.00002330658,0.003331614,0.0009952538,0.9886386,0.006348471],"study_design_scores_gemma":[0.0003396566,0.0002071805,0.001027085,0.00003182613,0.000002331722,0.000001052178,0.001305577,0.00006640352,0.004037428,0.0001376841,0.992659,0.0001847371],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6284873,0.02618627,0.001027293,0.2697758,0.01187391,0.01135344,0.02902444,0.001000264,0.02127131],"genre_scores_gemma":[0.8624,0.02350644,0.001234142,0.007612319,0.01131117,0.001744408,0.0370604,0.0003050909,0.05482607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2621635,"threshold_uncertainty_score":0.5096022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106638838175293,"score_gpt":0.423275749054378,"score_spread":0.3126118652368487,"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."}}