{"id":"W3203736931","doi":"10.1016/j.nicl.2021.102849","title":"Minimum detectable spinal cord atrophy with automatic segmentation: Investigations using an open-access dataset of healthy participants","year":2021,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Canada First Research Excellence Fund; Canada Research Chairs; Polytechnique Montréal; Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Atrophy; Spinal cord; Segmentation; Neuroscience; Medicine; Psychology; Physical medicine and rehabilitation; Pathology; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000511976,0.0001575479,0.0003278127,0.00006968116,0.0002959177,0.0004327653,0.0007315168,0.00005893532,0.0002256638],"category_scores_gemma":[0.001785564,0.000149984,0.00004292662,0.0008229114,0.0004503111,0.001377974,0.0003387815,0.0002941837,0.00002691145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003122209,"about_ca_system_score_gemma":0.0005837023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003190412,"about_ca_topic_score_gemma":0.0000593316,"domain_scores_codex":[0.9965248,0.001064064,0.0009437222,0.0008045941,0.0003554436,0.0003074478],"domain_scores_gemma":[0.9977787,0.0004061515,0.0005373391,0.0008308537,0.0001213152,0.0003256423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001483103,0.002344064,0.01571048,0.0001873126,0.00001851988,0.0003106322,0.00007447369,0.0001893003,0.9605014,0.0003191562,0.001465323,0.0173962],"study_design_scores_gemma":[0.006170397,0.01112709,0.2287305,0.0002114666,0.0002670965,0.0006820588,0.0004153358,0.1613075,0.5857993,0.0007412971,0.003686782,0.0008610754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957568,0.000007198085,0.002220322,0.0008215471,0.0003749763,0.0004137493,0.0002437681,0.00006908752,0.00009256491],"genre_scores_gemma":[0.9873172,0.00001483274,0.007675529,0.004717499,0.00007954268,0.00003646571,0.00009832605,0.00002930015,0.00003131225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3747021,"threshold_uncertainty_score":0.611617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.49214195009322,"score_gpt":0.5179122419491198,"score_spread":0.0257702918558998,"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."}}