{"id":"W2930864727","doi":"10.1016/j.wneu.2019.03.241","title":"Can Systemic Inflammatory Markers Be Used to Predict the Pathological Grade of Meningioma Before Surgery?","year":2019,"lang":"en","type":"article","venue":"World Neurosurgery","topic":"Meningioma and schwannoma management","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Receiver operating characteristic; Meningioma; Biomarker; Logistic regression; Pathological; Area under the curve; Mann–Whitney U test; Internal medicine; Cohort; Prospective cohort study; Predictive value of tests; Radiology; Gastroenterology; 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":[],"consensus_categories":[],"category_scores_codex":[0.001300151,0.0006635105,0.001545234,0.001324197,0.0005339119,0.001958453,0.0005481704,0.001699419,0.001873918],"category_scores_gemma":[0.006478125,0.0002982381,0.0007716336,0.0009069722,0.0005210663,0.001876154,0.0003259227,0.002430723,0.0009541179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003869709,"about_ca_system_score_gemma":0.001257084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00179498,"about_ca_topic_score_gemma":0.003493728,"domain_scores_codex":[0.9994604,0.0001464536,0.00009197892,0.00006071208,0.0001326382,0.0001078487],"domain_scores_gemma":[0.9976009,0.0007557627,0.0005944979,0.00008929439,0.0005992537,0.000360378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001607213,0.0004453939,0.8964109,0.0002802247,0.0002948165,0.001840653,0.0001294098,0.0005915671,0.002435926,0.0003755249,0.004625871,0.0909624],"study_design_scores_gemma":[0.0002726817,0.003392047,0.9401172,0.00174604,0.00118717,0.009199565,0.002190323,0.007905241,0.005693187,0.005762975,0.02234404,0.0001894519],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8410323,0.08915469,0.008002105,0.0348208,0.004879181,0.0001409055,0.0008329566,0.0002550661,0.02088198],"genre_scores_gemma":[0.9716827,0.01525878,0.004185377,0.003303108,0.003270913,0.00004958048,0.0004478779,0.00002827035,0.00177344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001958453,"threshold_uncertainty_score":0.006875992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050128564487996,"score_gpt":0.2432159929141941,"score_spread":0.2127147072693142,"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."}}