{"id":"W2198283839","doi":"10.1186/s12871-015-0165-y","title":"Codifying healthcare – big data and the issue of misclassification","year":2015,"lang":"en","type":"letter","venue":"BMC Anesthesiology","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto","funders":"","keywords":"Observational study; Documentation; Medical diagnosis; Anesthesiology; Variety (cybernetics); Medicine; Data science; Medical record; Process (computing); Health care; Computer science; Artificial intelligence; Pathology; Surgery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1701147,0.0005766422,0.001436427,0.003168491,0.002960206,0.005366701,0.003146404,0.01531756,0.003870742],"category_scores_gemma":[0.4702693,0.0006674076,0.001745593,0.004147833,0.008418215,0.008845733,0.004004403,0.01500595,0.002122981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006931361,"about_ca_system_score_gemma":0.006814122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01024889,"about_ca_topic_score_gemma":0.01040583,"domain_scores_codex":[0.7915278,0.1483888,0.01717732,0.007182521,0.03368936,0.002034184],"domain_scores_gemma":[0.3779057,0.5310342,0.03418854,0.02950113,0.0235235,0.003846914],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004312347,0.0001060275,0.03711101,0.002091924,0.000418044,0.002117498,0.008085471,0.000769572,0.0003374147,0.07692919,0.5923392,0.2792634],"study_design_scores_gemma":[0.0003266442,0.0003415211,0.02857478,0.01601491,0.0002905232,0.008045726,0.00685746,0.008065546,0.00118925,0.3479844,0.5819624,0.000346829],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.003185211,0.004444695,0.006578968,0.9779829,0.004383205,0.0001201889,0.000302898,0.00006687082,0.002935051],"genre_scores_gemma":[0.04520982,0.005557679,0.01236291,0.9213871,0.01319265,0.0004554194,0.0002422342,0.00009073773,0.00150146],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.8298853,"threshold_uncertainty_score":0.8996632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6291038405164439,"score_gpt":0.4766741644057179,"score_spread":0.152429676110726,"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."}}