{"id":"W2968102186","doi":"10.1007/s00415-019-09488-6","title":"A probabilistic atlas of the human inner ear’s bony labyrinth enables reliable atlas-based segmentation of the total fluid space","year":2019,"lang":"en","type":"article","venue":"Journal of Neurology","topic":"Vestibular and auditory disorders","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Bundesministerium für Bildung und Forschung","keywords":"Atlas (anatomy); Anatomy; Neuroradiology; Inner ear; Segmentation; Medicine; Neurology; Computer science; Artificial intelligence; Biology; Neuroscience","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":[0.00127306,0.0007566171,0.0006108787,0.001668965,0.0008117313,0.002671754,0.0008724012,0.001508748,0.002996481],"category_scores_gemma":[0.004002674,0.0008257024,0.0009172392,0.001482076,0.0007258319,0.001508587,0.002230234,0.001370496,0.001418076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007840086,"about_ca_system_score_gemma":0.003598289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006148133,"about_ca_topic_score_gemma":0.008184646,"domain_scores_codex":[0.9994134,0.0001290377,0.00004692865,0.000125484,0.0002292679,0.00005598324],"domain_scores_gemma":[0.9989094,0.000430526,0.0001278045,0.0002654157,0.0002008743,0.00006601297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007599563,0.0001111852,0.009148883,0.001034124,0.0002919251,0.001926787,0.001688738,0.1290366,0.3133858,0.0378193,0.01600973,0.488787],"study_design_scores_gemma":[0.0001081167,0.000421621,0.02747997,0.0003444802,0.0003214301,0.01342144,0.0006559116,0.6240676,0.1944456,0.0673582,0.07100988,0.0003658079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02420384,0.0004976474,0.9689567,0.0002423208,0.00004951532,0.0001096468,0.0008776978,0.002497111,0.002565471],"genre_scores_gemma":[0.3314675,0.0009115599,0.6613532,0.0001656107,0.00007324366,0.0002896092,0.001050177,0.001814499,0.002874679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006148133,"threshold_uncertainty_score":0.01222467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01217292850319374,"score_gpt":0.2390971900553638,"score_spread":0.2269242615521701,"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."}}