{"id":"W4394150836","doi":"10.6084/m9.figshare.17013913","title":"Identifying cases of spinal cord injury or disease in a primary care electronic medical record database","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Nursing Diagnosis and Documentation","field":"Nursing","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University Health Network; University of Toronto","funders":"","keywords":"Medicine; Primary care; Spinal cord injury; Spinal cord; Electronic medical record; Medical record; Electronic database; Database; Disease; Medical emergency; Computer science; Pathology; Internal medicine; Family medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003337733,0.0002443357,0.0005908008,0.003200939,0.0006400796,0.001015032,0.001025739,0.000497835,0.002024396],"category_scores_gemma":[0.01704356,0.0002557776,0.0003328288,0.003615883,0.0002438907,0.0006086337,0.0008321817,0.0002744636,0.000617051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002719258,"about_ca_system_score_gemma":0.00451317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1380898,"about_ca_topic_score_gemma":0.2405607,"domain_scores_codex":[0.9941203,0.001183874,0.001019037,0.0007910574,0.002510317,0.0003753674],"domain_scores_gemma":[0.9861141,0.00268883,0.004031441,0.001197828,0.005014133,0.0009537152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002288551,0.0001938157,0.9817486,0.0002919872,0.00009042332,0.0001937475,0.0002120275,0.0001236291,0.0005141106,0.00008631167,0.00439285,0.0119236],"study_design_scores_gemma":[0.00007433469,0.0001243844,0.9958188,0.00007248583,0.00004776942,0.000286675,0.0001421932,0.001417058,0.0002704113,0.00003361313,0.001705,0.000007333185],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9450889,0.0008395462,0.002283306,0.0005238836,0.00002583526,0.004676757,0.03968879,0.0001544914,0.006718596],"genre_scores_gemma":[0.9656574,0.0004304751,0.007247052,0.000435711,0.00005161737,0.001125549,0.02407217,0.00001054475,0.0009694446],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1380898,"threshold_uncertainty_score":0.274572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05413928119168778,"score_gpt":0.3839412484263772,"score_spread":0.3298019672346894,"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."}}