{"id":"W4385614914","doi":"10.1016/j.acra.2023.07.009","title":"Semiautomated Segmentation and Analysis of Airway Lumen in Pediatric Patients Using Ultra Short Echo Time MRI","year":2023,"lang":"en","type":"article","venue":"Academic Radiology","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Ottawa; Hospital for Sick Children; Children's Hospital of Eastern Ontario; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Ontario Trillium Foundation; Cystic Fibrosis Foundation","keywords":"Medicine; Airway; Echo (communications protocol); Segmentation; Radiology; Lumen (anatomy); Nuclear medicine; Computer science; Internal medicine; Artificial intelligence; Surgery","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":[0.0003073536,0.0001112462,0.0003633317,0.001015138,0.00003915452,0.000003143842,0.0001417092,0.0001317556,0.00005678308],"category_scores_gemma":[0.000009613346,0.0001102453,0.00006201117,0.002459946,0.00006257624,0.0001081015,0.00005300534,0.0002985477,0.00001925326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005752753,"about_ca_system_score_gemma":0.00006364645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000980796,"about_ca_topic_score_gemma":5.270314e-7,"domain_scores_codex":[0.9988403,0.0001327837,0.0003547916,0.0002577818,0.0001225085,0.0002918777],"domain_scores_gemma":[0.9995229,0.0001795,0.00009400668,0.0001134746,0.00003309517,0.00005704576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001065347,0.00001660673,0.9787124,0.000006216587,0.0001990359,5.419878e-7,0.0006622026,0.002022543,0.01302995,0.00008012482,0.0005066064,0.004753053],"study_design_scores_gemma":[0.0006936373,0.00002170995,0.3998019,0.000006869297,0.0003166065,3.741486e-7,0.0003384697,0.5945044,0.003183058,0.0009044168,0.00002191071,0.0002067435],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986334,0.00003489328,0.0005564252,0.00002336191,0.00002774549,0.0001680994,0.00005599978,0.00002467428,0.0004753447],"genre_scores_gemma":[0.9992114,0.0001931767,0.00004386361,0.00001311588,0.00005360605,0.0000121219,0.000401555,0.00001171707,0.00005939358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5924819,"threshold_uncertainty_score":0.4495673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618673471072022,"score_gpt":0.3118568327899139,"score_spread":0.2956700980791936,"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."}}