{"id":"W4403773194","doi":"10.1002/hbm.70054","title":"The Effect of Segmentation Method on Medial Temporal Lobe Subregion Volumes in Aging","year":2024,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health; Alzheimer Society; James S. McDonnell Foundation","keywords":"Temporal lobe; Entorhinal cortex; Perirhinal cortex; Segmentation; Neurodegeneration; Atrophy; Neuroscience; Hippocampal formation; Atlas (anatomy); Psychology; Medicine; Pathology; Anatomy; Artificial intelligence; Disease; Computer science; Epilepsy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.006165216,0.0008848904,0.0006327544,0.001291795,0.0006096067,0.001351501,0.0006492083,0.0007410065,0.001472301],"category_scores_gemma":[0.01826103,0.0005296986,0.0006968739,0.0007854787,0.0007123846,0.0007992419,0.0007075921,0.0005275817,0.0005003135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006696606,"about_ca_system_score_gemma":0.0005538733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003981096,"about_ca_topic_score_gemma":0.006856423,"domain_scores_codex":[0.9974924,0.0009798193,0.0002827599,0.0006347364,0.0005079733,0.0001022782],"domain_scores_gemma":[0.9918338,0.004567019,0.001140292,0.0009212763,0.00137418,0.0001635318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01278057,0.0005748146,0.1702089,0.001509822,0.002725868,0.0005689224,0.004113313,0.0298361,0.4707997,0.002511077,0.005319957,0.2990509],"study_design_scores_gemma":[0.0003311224,0.004082622,0.6149111,0.00029735,0.001166878,0.001809699,0.0008956715,0.1714597,0.1917085,0.005579955,0.007493274,0.0002641146],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9413833,0.001986126,0.0510775,0.000198944,0.0002770087,0.0003215653,0.0007625826,0.00148282,0.002510199],"genre_scores_gemma":[0.9424617,0.0004186551,0.05336569,0.0001577811,0.00004455967,0.0002754871,0.0009636325,0.001112442,0.001200152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006165216,"threshold_uncertainty_score":0.03260517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02566583048852369,"score_gpt":0.3761912433423483,"score_spread":0.3505254128538246,"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."}}