{"id":"W4412523007","doi":"10.1038/s41746-025-01846-1","title":"Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm","year":2025,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Center for Advancing Translational Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; National Eye Institute; National Institute of Mental Health; U.S. National Library of Medicine; Patient-Centered Outcomes Research Institute; National Institute on Aging; National Institutes of Health; U.S. Department of Veterans Affairs","keywords":"Lossless compression; Computer science; Generalized linear mixed model; Homomorphic encryption; Data mining; Algorithm; Encryption; Statistics; Econometrics; Data compression; Machine learning; Computer security; Mathematics","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":["metaresearch","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005471418,0.0002900038,0.0005114999,0.0008998907,0.0001091423,0.0001955675,0.009840694,0.00005975608,0.000004225872],"category_scores_gemma":[0.009195963,0.0002217744,0.0000213298,0.004017338,0.0004369502,0.001072873,0.02041962,0.0003695865,0.000008355553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000419336,"about_ca_system_score_gemma":0.0001729508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001852178,"about_ca_topic_score_gemma":0.0009240138,"domain_scores_codex":[0.9973701,0.00006082652,0.0005399624,0.0008793201,0.0007488052,0.0004010403],"domain_scores_gemma":[0.9957945,0.0005371149,0.0001873835,0.002970499,0.0004476926,0.00006281643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003798578,0.007208018,0.372015,0.0002288378,0.001684159,0.002347594,0.02021466,0.0002876796,0.0002964842,0.01001536,0.04222167,0.5431007],"study_design_scores_gemma":[0.03995093,0.008444286,0.2267436,0.01241357,0.0002226833,0.00005785834,0.0621993,0.5764093,0.00190126,0.05933549,0.009131349,0.003190374],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2977765,0.001470976,0.6032641,0.05742974,0.002853968,0.003461309,0.0002140826,0.002091808,0.03143757],"genre_scores_gemma":[0.9509619,0.00009736743,0.04812226,0.0002512235,0.00006180983,0.0001040841,0.0000268285,0.00001397233,0.0003604881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6531855,"threshold_uncertainty_score":0.99915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07732397268261708,"score_gpt":0.3788656639012991,"score_spread":0.301541691218682,"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."}}