{"id":"W4399929933","doi":"10.1002/ohn.868","title":"A Label‐Efficient Framework for Automated Sinonasal CT Segmentation in Image‐Guided Surgery","year":2024,"lang":"en","type":"article","venue":"Otolaryngology","topic":"Sinusitis and nasal conditions","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Image-guided surgery; Computer science; Artificial intelligence; Segmentation; Computer vision; Medicine; Radiology","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":[0.002575067,0.001094804,0.0008043169,0.001950057,0.0006930937,0.001519262,0.00217009,0.002048444,0.00164468],"category_scores_gemma":[0.004840023,0.0007266146,0.001201215,0.0009517704,0.0008381404,0.0013242,0.001521623,0.00138784,0.0009744002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001953672,"about_ca_system_score_gemma":0.00300613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0141422,"about_ca_topic_score_gemma":0.02455582,"domain_scores_codex":[0.9986801,0.0003425787,0.00007679772,0.0003675752,0.0004017068,0.0001312732],"domain_scores_gemma":[0.9980769,0.0008166518,0.0003103538,0.0002088623,0.000479565,0.0001075997],"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":[0.0005715508,0.0004162165,0.01083235,0.0002919664,0.0002145154,0.0002738844,0.0002967512,0.3438545,0.03786631,0.003579578,0.008956765,0.5928457],"study_design_scores_gemma":[0.00002878344,0.00007053244,0.001293787,0.0000179021,0.00002378007,0.0001270415,0.00002700274,0.9870978,0.007866624,0.001896876,0.00152942,0.00002044851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03201882,0.0003612724,0.9623505,0.0002631277,0.00003619264,0.0001771434,0.0003285726,0.003787512,0.0006769198],"genre_scores_gemma":[0.264751,0.0002249122,0.7306241,0.0003090842,0.00007275368,0.0004093236,0.001412027,0.0004749197,0.001721858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0141422,"threshold_uncertainty_score":0.02811974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03348577930956188,"score_gpt":0.354143778415956,"score_spread":0.3206579991063941,"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."}}