{"id":"W2981017557","doi":"10.1093/neuonc/noz175.323","title":"EPID-23. PURSUIT OF AN INTERNATIONAL LANGUAGE OF GLIOMA RESEARCH: COMMON DATA ELEMENTS FOR THE LONGITUDINAL STUDY OF ADULT MALIGNANT GLIOMA","year":2019,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"","keywords":"Glioma; Computer science; Electronic data capture; Medical physics; Medicine; Clinical trial; Translational research; Data science; Data mining; Pathology","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":[0.1424688,0.0008070451,0.00163566,0.006603655,0.001514403,0.005266158,0.002806791,0.002229579,0.02131876],"category_scores_gemma":[0.2931815,0.001471946,0.003665838,0.009786994,0.001716531,0.004422028,0.009222536,0.003363681,0.008601175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004640101,"about_ca_system_score_gemma":0.02743626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005655852,"about_ca_topic_score_gemma":0.005526942,"domain_scores_codex":[0.895544,0.05740058,0.03315884,0.004489971,0.007516453,0.001890079],"domain_scores_gemma":[0.6360221,0.1784993,0.03498543,0.08483356,0.05723204,0.008427498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005147709,0.0005812606,0.07508407,0.01650519,0.00167982,0.0004610673,0.004436885,0.003510033,0.002010171,0.0887872,0.5512413,0.2505553],"study_design_scores_gemma":[0.001771043,0.0007955499,0.07334815,0.00522003,0.0006749595,0.0002903687,0.0008364063,0.001608822,0.00358328,0.02307015,0.8886051,0.0001961274],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01333876,0.001211317,0.1345317,0.01670224,0.001382058,0.02136209,0.7744639,0.00762294,0.02938503],"genre_scores_gemma":[0.06380644,0.0015583,0.3779829,0.008684664,0.000648451,0.08264968,0.4545193,0.002392356,0.007757957],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1424688,"threshold_uncertainty_score":0.7534562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07409258431600414,"score_gpt":0.3905870412698593,"score_spread":0.3164944569538551,"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."}}