{"id":"W2027496075","doi":"10.1371/annotation/21fb1298-a831-423f-a247-205641dda40c","title":"Correction: Variance in Brain Volume with Advancing Age: Implications for Defining the Limits of Normality","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Amorfix Life Sciences; AstraZeneca; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; Alzheimer's Association","keywords":"Percentile; Normality; Statistics; Brain size; Percentile rank; Sample size determination; Normal distribution; Linear regression; Standard deviation; Regression analysis; Volume (thermodynamics); Ageing; Medicine; Mathematics; Magnetic resonance imaging; Internal medicine; Radiology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06590143,0.002917008,0.003105596,0.004667902,0.002113627,0.004857807,0.006972126,0.004400203,0.04422384],"category_scores_gemma":[0.411897,0.001062084,0.003073794,0.008029465,0.005822394,0.004908369,0.005044251,0.007880773,0.005087989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002751946,"about_ca_system_score_gemma":0.00657583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007384731,"about_ca_topic_score_gemma":0.007469316,"domain_scores_codex":[0.9296583,0.03654039,0.008225599,0.01190426,0.01138239,0.002289114],"domain_scores_gemma":[0.6942372,0.2399746,0.01142906,0.03599169,0.01593307,0.002434465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.008534691,0.0006333933,0.07718151,0.0105162,0.006916794,0.008242824,0.009765574,0.01256106,0.0112713,0.102549,0.3398127,0.4120149],"study_design_scores_gemma":[0.001356648,0.002689804,0.1817825,0.007040067,0.002412651,0.009808129,0.004547507,0.1382322,0.01921892,0.2682291,0.3638144,0.0008680492],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07454203,0.009100516,0.8101181,0.01469424,0.05565349,0.002638622,0.01024101,0.008614857,0.01439711],"genre_scores_gemma":[0.517206,0.002285611,0.424257,0.006318166,0.005016087,0.00488741,0.002578252,0.005203817,0.0322478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06590143,"threshold_uncertainty_score":0.3485242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03955054851963399,"score_gpt":0.3093599903522006,"score_spread":0.2698094418325667,"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."}}