{"id":"W1917558546","doi":"10.1038/ejhg.2015.165","title":"Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research","year":2015,"lang":"en","type":"article","venue":"European Journal of Human Genetics","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Münchner Zentrum für Gesundheitswissenschaften; Terveyden ja hyvinvoinnin laitos; Tartu Ülikool; Karolinska Institutet; Queen's University; Helmholtz Zentrum München; National Institute for Health and Care Research; Bundesministerium für Bildung und Forschung; University of Oxford; Queen's University Belfast; Innovative Medicines Initiative; Imperial College London; European Federation of Pharmaceutical Industries and Associations; Lunds Universitet; King's College London; Swedish e-Science Research Centre; Oulun Yliopisto; European Commission; Broad Institute","keywords":"Biobank; Data science; Disparate system; Sample (material); Computer science; Data access; Summit; Biorepository; Data integration; Data mining; Bioinformatics; Database; Biology; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","sts","open_science","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04866909,0.0001905345,0.0005377006,0.0002129781,0.0003919117,0.0005615113,0.002152857,0.0001209938,0.0000142419],"category_scores_gemma":[0.02844092,0.0001386972,0.0000492407,0.0002590655,0.003269927,0.0002118818,0.008034287,0.004567261,0.00002918794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003426653,"about_ca_system_score_gemma":0.0006452051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006343867,"about_ca_topic_score_gemma":0.00003848578,"domain_scores_codex":[0.9933208,0.002030716,0.001528963,0.000780804,0.001711841,0.0006269298],"domain_scores_gemma":[0.9896587,0.005204617,0.0003213909,0.001967764,0.00111056,0.001736991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005669442,0.002419958,0.4613723,0.0009222617,0.001394736,0.01142574,0.0359982,0.00003653907,0.01506054,0.0008407944,0.04908835,0.4157711],"study_design_scores_gemma":[0.02543357,0.04384714,0.5924962,0.01181478,0.000575687,0.00276119,0.01579215,0.02001768,0.001869839,0.03120654,0.2526876,0.001497618],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874489,0.00343833,0.00335719,0.003232954,0.0003402754,0.0002518602,0.0001087788,0.00001245987,0.00180923],"genre_scores_gemma":[0.9386414,0.004742184,0.0524372,0.0004381807,0.002363777,3.475747e-7,0.0001006063,0.00007822342,0.001198081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4142735,"threshold_uncertainty_score":0.9999886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8777170357882732,"score_gpt":0.7046801127487128,"score_spread":0.1730369230395604,"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."}}