{"id":"W4398168049","doi":"10.1002/lary.31435","title":"Creating Patient‐Specific 3D‐Printed Airway Models for Slide Tracheoplasty","year":2024,"lang":"en","type":"article","venue":"The Laryngoscope","topic":"Tracheal and airway disorders","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Institute on Deafness and Other Communication Disorders","keywords":"3d printed; Airway; Medicine; 3d model; Computer science; Biomedical engineering; Surgery; Artificial intelligence","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":[0.002079414,0.0007021959,0.0003744854,0.0008830433,0.0004849017,0.001059466,0.001060877,0.0009636644,0.02481251],"category_scores_gemma":[0.003885122,0.0008570032,0.001053552,0.0003119602,0.0005044069,0.0007579373,0.001152142,0.001399368,0.00717487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002853862,"about_ca_system_score_gemma":0.001184457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004137894,"about_ca_topic_score_gemma":0.001073547,"domain_scores_codex":[0.9992235,0.0001401609,0.0001158763,0.00006674505,0.0004148908,0.00003886364],"domain_scores_gemma":[0.9988708,0.0005242474,0.00007881923,0.0002476913,0.0002208049,0.00005765609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001067446,0.001064003,0.00382521,0.003840288,0.0001035238,0.002685719,0.001434433,0.04144811,0.3288488,0.01869471,0.05106055,0.5459272],"study_design_scores_gemma":[0.0003049294,0.002446549,0.005238349,0.001206226,0.0001716477,0.008561849,0.0004306173,0.05335949,0.347124,0.006168006,0.5746244,0.0003638995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02860086,0.001857171,0.921158,0.00086274,0.001102972,0.008334362,0.002938004,0.003824797,0.03132105],"genre_scores_gemma":[0.0928246,0.003292626,0.8667147,0.0006514254,0.0001022741,0.01291316,0.002772395,0.00123361,0.01949522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02481251,"threshold_uncertainty_score":0.08300614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02267809103452979,"score_gpt":0.2668097809963859,"score_spread":0.2441316899618561,"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."}}