{"id":"W2900423557","doi":"10.1093/neuonc/noy148.777","title":"NIMG-51. THE IMPACT OF FUNCTIONAL MAGNETIC RESONANCE IMAGING ON CLINICAL OUTCOMES IN A PROPENSITY-MATCHED LOW GRADE GLIOMA COHORT","year":2018,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Medicine; Functional magnetic resonance imaging; Propensity score matching; Magnetic resonance imaging; Cohort; Retrospective cohort study; Glioma; Cohort study; Surgery; Internal medicine; Radiology","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":[0.001021109,0.0002054413,0.0002965367,0.0004179089,0.0002500647,0.0003862412,0.0002723607,0.0003003319,0.00137701],"category_scores_gemma":[0.0025828,0.000104717,0.0003331479,0.0004259518,0.0002171639,0.0002770789,0.0004139097,0.0001902266,0.0003915236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002889209,"about_ca_system_score_gemma":0.000331964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002096381,"about_ca_topic_score_gemma":0.003236828,"domain_scores_codex":[0.999508,0.0001926831,0.00003137464,0.0001086486,0.00008924431,0.00007003876],"domain_scores_gemma":[0.9992023,0.000126902,0.0002996836,0.0001338402,0.00006028624,0.0001769792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001618892,0.00009125922,0.9895201,0.0000121478,0.0001248975,0.0001759896,0.00003401754,0.00009057685,0.001082025,0.00003747686,0.0003445421,0.00686805],"study_design_scores_gemma":[0.0001056186,0.0007735736,0.9972796,0.000004768478,0.00009353994,0.0004037927,0.00004689496,0.0002977494,0.0003029544,0.00004492523,0.000641993,0.000004571008],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987242,0.0001237598,0.0001954139,0.00005220281,0.000007322987,0.00002899907,0.0003820757,0.000008767419,0.0004771744],"genre_scores_gemma":[0.9985809,0.00005294811,0.0002108559,0.00006827781,0.00001155326,0.0000321468,0.0008512997,0.000006630412,0.0001853687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002096381,"threshold_uncertainty_score":0.005400181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04926363825930713,"score_gpt":0.3665862359327058,"score_spread":0.3173225976733987,"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."}}