{"id":"W4405095249","doi":"10.1016/j.expneurol.2024.115100","title":"Data reporting quality and semantic interoperability increase with community-based data elements (CoDEs). Analysis of the open data commons for spinal cord injury (ODC-SCI)","year":2024,"lang":"en","type":"article","venue":"Experimental Neurology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; Women and Children’s Health Research Institute","funders":"National Institute of Neurological Disorders and Stroke; Division of Mathematical Sciences; National Institutes of Health; Canada Excellence Research Chairs, Government of Canada; University of California; Canada Research Chairs; Craig H. Neilsen Foundation; Wings for Life; Canadian Institutes of Health Research; U.S. Department of Veterans Affairs","keywords":"Interoperability; Spinal cord injury; Commons; Computer science; Data quality; Quality (philosophy); Data mining; Spinal cord; Psychology; Neuroscience; World Wide Web; Engineering; Political science; Operations management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.4240972,0.001110505,0.002111778,0.02765833,0.00462118,0.01519259,0.006092986,0.004149086,0.003358412],"category_scores_gemma":[0.7376728,0.001887429,0.003328533,0.03036849,0.01413829,0.02475655,0.02230768,0.00715398,0.001160573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01201159,"about_ca_system_score_gemma":0.023317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009821083,"about_ca_topic_score_gemma":0.008563106,"domain_scores_codex":[0.374157,0.3258444,0.1084752,0.03825672,0.1494203,0.00384638],"domain_scores_gemma":[0.07277675,0.6039093,0.09327244,0.1405421,0.08693679,0.00256267],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000629397,0.0003647318,0.3204926,0.01554214,0.001644608,0.0004958356,0.06037665,0.005489376,0.005180625,0.1613577,0.03249656,0.3959299],"study_design_scores_gemma":[0.0002545542,0.0006409971,0.2367792,0.02158481,0.001079112,0.001697505,0.03512994,0.01290538,0.01532226,0.3087755,0.3645979,0.001232783],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2148243,0.01069595,0.6252866,0.06039284,0.002124694,0.006561582,0.02653629,0.003937522,0.0496402],"genre_scores_gemma":[0.5440841,0.003365289,0.4098219,0.009590309,0.001020572,0.007453745,0.02119413,0.001326695,0.002143201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.993907,"threshold_uncertainty_score":0.7101907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2793202228920424,"score_gpt":0.485861387772787,"score_spread":0.2065411648807446,"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."}}