{"id":"W3006718263","doi":"10.1016/j.bja.2020.01.012","title":"Real-world evaluation of enhanced recovery after surgery: big data under the microscope","year":2020,"lang":"en","type":"letter","venue":"British Journal of Anaesthesia","topic":"Enhanced Recovery After Surgery","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Ottawa Hospital Anesthesia Alternate Funds Association","keywords":"Microscope; Computer science; Medicine; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.003971424,0.0002252026,0.000391933,0.0003958594,0.0008326588,0.001849703,0.0005975458,0.005010244,0.003927354],"category_scores_gemma":[0.02495492,0.0001820204,0.0003017584,0.0006238085,0.0008392375,0.002434396,0.00120241,0.003149048,0.001725037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001154921,"about_ca_system_score_gemma":0.0008762464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003219078,"about_ca_topic_score_gemma":0.006015904,"domain_scores_codex":[0.9973626,0.001360113,0.0002783592,0.000140749,0.0006755565,0.0001826405],"domain_scores_gemma":[0.9752126,0.01679702,0.001774705,0.001107231,0.003140802,0.0019676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001737118,0.0002180126,0.06406616,0.0005935913,0.0001484918,0.01457311,0.002574881,0.002564998,0.002489944,0.006186174,0.783717,0.1211305],"study_design_scores_gemma":[0.0005165931,0.001373123,0.09553357,0.001634549,0.0001557567,0.02092409,0.02135134,0.02910897,0.003676162,0.05386062,0.7714579,0.000407482],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.06510017,0.003083348,0.004376387,0.9039071,0.006023231,0.00008074368,0.002728464,0.000250097,0.01445054],"genre_scores_gemma":[0.7425887,0.006209908,0.01416607,0.2103518,0.01501564,0.0002411236,0.002017868,0.000212244,0.009196671],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.005010244,"threshold_uncertainty_score":0.02100319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07232144458966068,"score_gpt":0.300327218788195,"score_spread":0.2280057741985343,"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."}}