{"id":"W4286785473","doi":"10.33448/rsd-v11i9.31721","title":"Development of a customized three-dimensional airway model","year":2022,"lang":"en","type":"article","venue":"Research Society and Development","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital","funders":"","keywords":"Intraclass correlation; Confidence interval; Computed tomography; Nuclear medicine; Airway; Replicate; Software; 3d model; Biomedical engineering; Computer science; Artificial intelligence; Medicine; Mathematics; Radiology; Statistics; Surgery","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":[0.0007502486,0.0005104259,0.0004793493,0.0006077011,0.0001723029,0.001035049,0.001186014,0.001025983,0.001717618],"category_scores_gemma":[0.001819285,0.0005392442,0.001430283,0.0003020833,0.0003455705,0.0006450176,0.001049096,0.000673754,0.0007575976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003069031,"about_ca_system_score_gemma":0.0007338858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008389497,"about_ca_topic_score_gemma":0.0008591478,"domain_scores_codex":[0.999487,0.00006275194,0.00005453054,0.00008889976,0.0002780017,0.00002872059],"domain_scores_gemma":[0.9993656,0.0002326577,0.00005685498,0.0001815387,0.0001283148,0.00003504775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001687978,0.0001691376,0.00430482,0.0006320574,0.0001452966,0.001624201,0.0003867912,0.4468958,0.4060012,0.00642081,0.002506363,0.1307447],"study_design_scores_gemma":[0.00004352053,0.0004782218,0.003897414,0.00008845598,0.0001331944,0.003267989,0.00007644977,0.8539984,0.1112187,0.00188532,0.02476501,0.0001472844],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0626302,0.0002657256,0.9328123,0.0001194508,0.0001424584,0.0002873365,0.0003701331,0.001384205,0.001988182],"genre_scores_gemma":[0.4442679,0.0006634418,0.5493701,0.0001629735,0.00004190601,0.0005619971,0.001202523,0.0004134747,0.003315702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001717618,"threshold_uncertainty_score":0.005746007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06439441190661042,"score_gpt":0.3658855524025116,"score_spread":0.3014911404959011,"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."}}