{"id":"W2765674039","doi":"10.1310/sci2304-324","title":"Creation of an Algorithm to Identify Non-traumatic Spinal Cord Dysfunction Patients in Canada Using Administrative Health Data","year":2017,"lang":"en","type":"article","venue":"Topics in Spinal Cord Injury Rehabilitation","topic":"Spinal Cord Injury Research","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Praxis Spinal Cord Institute; Hotchkiss Brain Institute; Institute for Work & Health; University of Calgary; Institute for Clinical Evaluative Sciences; Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Western Economic Diversification Canada; Health Canada; University of Toronto; Toronto Rehabilitation Institute; Ontario Neurotrauma Foundation; Rick Hansen Institute","keywords":"Medicine; Diagnosis code; Etiology; Rehabilitation; Medical diagnosis; Tetraplegia; Paraplegia; Ambulatory; Cohort; Disease; Health care; Acute care; Spinal cord injury; Pediatrics; Physical therapy; Spinal cord; Surgery; Psychiatry; Population; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006491304,0.001044113,0.001133979,0.008047914,0.002184501,0.003224375,0.002744844,0.0009476768,0.00139668],"category_scores_gemma":[0.0225825,0.0006004497,0.0009165696,0.00498536,0.0005545539,0.0009778012,0.0017742,0.001180361,0.0004505996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01245957,"about_ca_system_score_gemma":0.03222419,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6433871,"about_ca_topic_score_gemma":0.6833668,"domain_scores_codex":[0.9961613,0.0007013991,0.0006142742,0.0007693538,0.001268756,0.0004848107],"domain_scores_gemma":[0.9881448,0.002390339,0.001231482,0.0004057772,0.007244514,0.0005832531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002052275,0.000376401,0.8171154,0.0002477938,0.0002709701,0.0004643535,0.0004376125,0.01820622,0.001027394,0.001802127,0.01701364,0.1428329],"study_design_scores_gemma":[0.0003460874,0.0002171668,0.4039764,0.0003007283,0.0003090616,0.001118693,0.001589305,0.5681844,0.004266431,0.003720332,0.01582825,0.0001430545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5978551,0.001776503,0.3342823,0.008526627,0.0002477684,0.01478939,0.02537543,0.004179496,0.01296741],"genre_scores_gemma":[0.4769803,0.0005497174,0.5059577,0.0005890449,0.00004995558,0.001929496,0.01275625,0.00008450437,0.001103056],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3566129,"threshold_uncertainty_score":0.7174262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1598397197751382,"score_gpt":0.5087330772308313,"score_spread":0.3488933574556931,"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."}}